Overview

Dataset statistics

Number of variables34
Number of observations5800
Missing cells20378
Missing cells (%)10.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory272.0 B

Variable types

Numeric8
Categorical24
DateTime2

Warnings

cnt has constant value "United States" Constant
gen has a high cardinality: 117 distinct values High cardinality
sp has a high cardinality: 193 distinct values High cardinality
ssp has a high cardinality: 275 distinct values High cardinality
en has a high cardinality: 218 distinct values High cardinality
rec has a high cardinality: 394 distinct values High cardinality
loc has a high cardinality: 2456 distinct values High cardinality
alt has a high cardinality: 245 distinct values High cardinality
type has a high cardinality: 276 distinct values High cardinality
url has a high cardinality: 5800 distinct values High cardinality
file has a high cardinality: 5800 distinct values High cardinality
file-name has a high cardinality: 5800 distinct values High cardinality
sono has a high cardinality: 5800 distinct values High cardinality
uploaded has a high cardinality: 1868 distinct values High cardinality
also has a high cardinality: 1089 distinct values High cardinality
rmk has a high cardinality: 3336 distinct values High cardinality
lng is highly correlated with latHigh correlation
pred is highly correlated with genderHigh correlation
age is highly correlated with lic and 2 other fieldsHigh correlation
lic is highly correlated with age and 3 other fieldsHigh correlation
month is highly correlated with ageHigh correlation
playback-used is highly correlated with lic and 2 other fieldsHigh correlation
bird-seen is highly correlated with lic and 2 other fieldsHigh correlation
lat is highly correlated with lngHigh correlation
id is highly correlated with age and 3 other fieldsHigh correlation
gender is highly correlated with predHigh correlation
lic is highly correlated with playback-used and 3 other fieldsHigh correlation
q is highly correlated with cntHigh correlation
playback-used is highly correlated with lic and 2 other fieldsHigh correlation
bird-seen is highly correlated with lic and 2 other fieldsHigh correlation
length is highly correlated with cntHigh correlation
cnt is highly correlated with lic and 7 other fieldsHigh correlation
pred is highly correlated with cntHigh correlation
age is highly correlated with lic and 1 other fieldsHigh correlation
gender is highly correlated with cntHigh correlation
ssp has 4974 (85.8%) missing values Missing
lat has 90 (1.6%) missing values Missing
lng has 90 (1.6%) missing values Missing
alt has 113 (1.9%) missing values Missing
time has 765 (13.2%) missing values Missing
rmk has 2007 (34.6%) missing values Missing
gender has 5369 (92.6%) missing values Missing
age has 5350 (92.2%) missing values Missing
hour has 765 (13.2%) missing values Missing
minute has 765 (13.2%) missing values Missing
url is uniformly distributed Uniform
file is uniformly distributed Uniform
file-name is uniformly distributed Uniform
sono is uniformly distributed Uniform
df_index has unique values Unique
id has unique values Unique
url has unique values Unique
file has unique values Unique
file-name has unique values Unique
sono has unique values Unique
minute has 1468 (25.3%) zeros Zeros

Reproduction

Analysis started2021-05-18 03:29:20.108602
Analysis finished2021-05-18 03:29:59.877376
Duration39.77 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct5800
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31596.66534
Minimum96
Maximum56349
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.4 KiB
2021-05-17T23:30:00.037533image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum96
5-th percentile5197.6
Q120197.75
median30148
Q344815.25
95-th percentile53881.95
Maximum56349
Range56253
Interquartile range (IQR)24617.5

Descriptive statistics

Standard deviation14833.50656
Coefficient of variation (CV)0.4694643056
Kurtosis-1.073955198
Mean31596.66534
Median Absolute Deviation (MAD)12458.5
Skewness-0.1065286806
Sum183260659
Variance220032916.7
MonotonicityStrictly increasing
2021-05-17T23:30:00.222378image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
286741
 
< 0.1%
218401
 
< 0.1%
238851
 
< 0.1%
218361
 
< 0.1%
481701
 
< 0.1%
346931
 
< 0.1%
562841
 
< 0.1%
464041
 
< 0.1%
402571
 
< 0.1%
218241
 
< 0.1%
Other values (5790)5790
99.8%
ValueCountFrequency (%)
961
< 0.1%
971
< 0.1%
1071
< 0.1%
1081
< 0.1%
1101
< 0.1%
1181
< 0.1%
1291
< 0.1%
1321
< 0.1%
1331
< 0.1%
1341
< 0.1%
ValueCountFrequency (%)
563491
< 0.1%
563471
< 0.1%
563401
< 0.1%
563281
< 0.1%
563001
< 0.1%
562921
< 0.1%
562901
< 0.1%
562871
< 0.1%
562851
< 0.1%
562841
< 0.1%

id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct5800
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean284724.9333
Minimum1136
Maximum645833
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.4 KiB
2021-05-17T23:30:00.437208image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1136
5-th percentile31117.95
Q1131032.25
median287950.5
Q3416548.25
95-th percentile589361.05
Maximum645833
Range644697
Interquartile range (IQR)285516

Descriptive statistics

Standard deviation176525.3395
Coefficient of variation (CV)0.6199855331
Kurtosis-1.013163156
Mean284724.9333
Median Absolute Deviation (MAD)149852
Skewness0.2401113325
Sum1651404613
Variance3.11611955 × 1010
MonotonicityNot monotonic
2021-05-17T23:30:00.636896image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
348171
 
< 0.1%
2533251
 
< 0.1%
5826721
 
< 0.1%
138351
 
< 0.1%
4785991
 
< 0.1%
1734461
 
< 0.1%
5809971
 
< 0.1%
137001
 
< 0.1%
1113091
 
< 0.1%
5482251
 
< 0.1%
Other values (5790)5790
99.8%
ValueCountFrequency (%)
11361
< 0.1%
11401
< 0.1%
11571
< 0.1%
11601
< 0.1%
11911
< 0.1%
11931
< 0.1%
11941
< 0.1%
11951
< 0.1%
11971
< 0.1%
11981
< 0.1%
ValueCountFrequency (%)
6458331
< 0.1%
6458161
< 0.1%
6457971
< 0.1%
6457931
< 0.1%
6455331
< 0.1%
6455231
< 0.1%
6455221
< 0.1%
6455201
< 0.1%
6455191
< 0.1%
6451491
< 0.1%

gen
Categorical

HIGH CARDINALITY

Distinct117
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size45.4 KiB
Setophaga
 
381
Vireo
 
211
Empidonax
 
173
Geothlypis
 
144
Pipilo
 
144
Other values (112)
4747 

Length

Max length15
Median length9
Mean length8.489310345
Min length4

Characters and Unicode

Total characters49238
Distinct characters43
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBranta
2nd rowBranta
3rd rowBranta
4th rowBranta
5th rowBranta

Common Values

ValueCountFrequency (%)
Setophaga381
 
6.6%
Vireo211
 
3.6%
Empidonax173
 
3.0%
Geothlypis144
 
2.5%
Pipilo144
 
2.5%
Melospiza141
 
2.4%
Cyanocitta141
 
2.4%
Troglodytes127
 
2.2%
Corvus114
 
2.0%
Poecile112
 
1.9%
Other values (107)4112
70.9%

Length

2021-05-17T23:30:01.025344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
setophaga381
 
6.6%
vireo211
 
3.6%
empidonax173
 
3.0%
pipilo144
 
2.5%
geothlypis144
 
2.5%
melospiza141
 
2.4%
cyanocitta141
 
2.4%
troglodytes127
 
2.2%
corvus114
 
2.0%
poecile112
 
1.9%
Other values (107)4112
70.9%

Most occurring characters

ValueCountFrequency (%)
a4852
 
9.9%
o4697
 
9.5%
e3886
 
7.9%
i3840
 
7.8%
s3509
 
7.1%
l2789
 
5.7%
r2748
 
5.6%
t2649
 
5.4%
u2466
 
5.0%
p2180
 
4.4%
Other values (33)15622
31.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter43438
88.2%
Uppercase Letter5800
 
11.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a4852
11.2%
o4697
10.8%
e3886
 
8.9%
i3840
 
8.8%
s3509
 
8.1%
l2789
 
6.4%
r2748
 
6.3%
t2649
 
6.1%
u2466
 
5.7%
p2180
 
5.0%
Other values (13)9822
22.6%
Uppercase Letter
ValueCountFrequency (%)
S884
15.2%
P866
14.9%
C835
14.4%
M548
9.4%
T513
8.8%
A316
 
5.4%
L271
 
4.7%
V227
 
3.9%
E198
 
3.4%
G185
 
3.2%
Other values (10)957
16.5%

Most occurring scripts

ValueCountFrequency (%)
Latin49238
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a4852
 
9.9%
o4697
 
9.5%
e3886
 
7.9%
i3840
 
7.8%
s3509
 
7.1%
l2789
 
5.7%
r2748
 
5.6%
t2649
 
5.4%
u2466
 
5.0%
p2180
 
4.4%
Other values (33)15622
31.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII49238
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a4852
 
9.9%
o4697
 
9.5%
e3886
 
7.9%
i3840
 
7.8%
s3509
 
7.1%
l2789
 
5.7%
r2748
 
5.6%
t2649
 
5.4%
u2466
 
5.0%
p2180
 
4.4%
Other values (33)15622
31.7%

sp
Categorical

HIGH CARDINALITY

Distinct193
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size45.4 KiB
americana
 
152
ludovicianus
 
132
carolinensis
 
128
canadensis
 
108
melodia
 
95
Other values (188)
5185 

Length

Max length16
Median length9
Mean length8.723275862
Min length4

Characters and Unicode

Total characters50595
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowcanadensis
2nd rowcanadensis
3rd rowcanadensis
4th rowcanadensis
5th rowcanadensis

Common Values

ValueCountFrequency (%)
americana152
 
2.6%
ludovicianus132
 
2.3%
carolinensis128
 
2.2%
canadensis108
 
1.9%
melodia95
 
1.6%
cristata94
 
1.6%
bicolor84
 
1.4%
caerulea80
 
1.4%
aedon76
 
1.3%
cardinalis76
 
1.3%
Other values (183)4775
82.3%

Length

2021-05-17T23:30:01.370408image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
americana152
 
2.6%
ludovicianus132
 
2.3%
carolinensis128
 
2.2%
canadensis108
 
1.9%
melodia95
 
1.6%
cristata94
 
1.6%
bicolor84
 
1.4%
caerulea80
 
1.4%
aedon76
 
1.3%
cardinalis76
 
1.3%
Other values (183)4775
82.3%

Most occurring characters

ValueCountFrequency (%)
a6389
12.6%
i6160
12.2%
s4764
9.4%
r3657
 
7.2%
c3630
 
7.2%
l3463
 
6.8%
e3447
 
6.8%
u3150
 
6.2%
n3146
 
6.2%
o2707
 
5.4%
Other values (16)10082
19.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter50595
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a6389
12.6%
i6160
12.2%
s4764
9.4%
r3657
 
7.2%
c3630
 
7.2%
l3463
 
6.8%
e3447
 
6.8%
u3150
 
6.2%
n3146
 
6.2%
o2707
 
5.4%
Other values (16)10082
19.9%

Most occurring scripts

ValueCountFrequency (%)
Latin50595
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a6389
12.6%
i6160
12.2%
s4764
9.4%
r3657
 
7.2%
c3630
 
7.2%
l3463
 
6.8%
e3447
 
6.8%
u3150
 
6.2%
n3146
 
6.2%
o2707
 
5.4%
Other values (16)10082
19.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII50595
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a6389
12.6%
i6160
12.2%
s4764
9.4%
r3657
 
7.2%
c3630
 
7.2%
l3463
 
6.8%
e3447
 
6.8%
u3150
 
6.2%
n3146
 
6.2%
o2707
 
5.4%
Other values (16)10082
19.9%

ssp
Categorical

HIGH CARDINALITY
MISSING

Distinct275
Distinct (%)33.3%
Missing4974
Missing (%)85.8%
Memory size45.4 KiB
carolinensis
 
19
bessophilus
 
18
deserticola
 
14
ludovicianus
 
13
arizonae
 
12
Other values (270)
750 

Length

Max length26
Median length8
Mean length8.711864407
Min length5

Characters and Unicode

Total characters7196
Distinct characters44
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103 ?
Unique (%)12.5%

Sample

1st rowparvipes
2nd rowcanadensis
3rd rowinterior
4th rowmaxima
5th rowmaxima or interior

Common Values

ValueCountFrequency (%)
carolinensis19
 
0.3%
bessophilus18
 
0.3%
deserticola14
 
0.2%
ludovicianus13
 
0.2%
arizonae12
 
0.2%
magister11
 
0.2%
gilvus11
 
0.2%
occidentalis11
 
0.2%
aedon10
 
0.2%
montanus10
 
0.2%
Other values (265)697
 
12.0%
(Missing)4974
85.8%

Length

2021-05-17T23:30:01.749671image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
type32
 
3.6%
carolinensis19
 
2.2%
bessophilus18
 
2.0%
deserticola14
 
1.6%
ludovicianus13
 
1.5%
arizonae12
 
1.4%
occidentalis12
 
1.4%
montanus12
 
1.4%
gilvus11
 
1.2%
magister11
 
1.2%
Other values (250)727
82.5%

Most occurring characters

ValueCountFrequency (%)
i827
11.5%
a775
10.8%
s741
10.3%
e641
8.9%
l517
 
7.2%
n469
 
6.5%
o451
 
6.3%
c417
 
5.8%
r407
 
5.7%
u370
 
5.1%
Other values (34)1581
22.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7059
98.1%
Space Separator55
 
0.8%
Decimal Number34
 
0.5%
Uppercase Letter28
 
0.4%
Other Punctuation13
 
0.2%
Dash Punctuation3
 
< 0.1%
Open Punctuation2
 
< 0.1%
Close Punctuation2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i827
11.7%
a775
11.0%
s741
10.5%
e641
9.1%
l517
 
7.3%
n469
 
6.6%
o451
 
6.4%
c417
 
5.9%
r407
 
5.8%
u370
 
5.2%
Other values (15)1444
20.5%
Uppercase Letter
ValueCountFrequency (%)
T16
57.1%
P4
 
14.3%
O3
 
10.7%
Y2
 
7.1%
S1
 
3.6%
J1
 
3.6%
X1
 
3.6%
Decimal Number
ValueCountFrequency (%)
29
26.5%
39
26.5%
57
20.6%
15
14.7%
43
 
8.8%
01
 
2.9%
Other Punctuation
ValueCountFrequency (%)
?11
84.6%
/2
 
15.4%
Space Separator
ValueCountFrequency (%)
55
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3
100.0%
Open Punctuation
ValueCountFrequency (%)
(2
100.0%
Close Punctuation
ValueCountFrequency (%)
)2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7087
98.5%
Common109
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i827
11.7%
a775
10.9%
s741
10.5%
e641
9.0%
l517
 
7.3%
n469
 
6.6%
o451
 
6.4%
c417
 
5.9%
r407
 
5.7%
u370
 
5.2%
Other values (22)1472
20.8%
Common
ValueCountFrequency (%)
55
50.5%
?11
 
10.1%
29
 
8.3%
39
 
8.3%
57
 
6.4%
15
 
4.6%
-3
 
2.8%
43
 
2.8%
/2
 
1.8%
(2
 
1.8%
Other values (2)3
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII7196
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i827
11.5%
a775
10.8%
s741
10.3%
e641
8.9%
l517
 
7.2%
n469
 
6.5%
o451
 
6.3%
c417
 
5.8%
r407
 
5.7%
u370
 
5.1%
Other values (34)1581
22.0%

en
Categorical

HIGH CARDINALITY

Distinct218
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size45.4 KiB
Song Sparrow
 
95
Blue Jay
 
94
Carolina Wren
 
93
Northern Cardinal
 
76
House Wren
 
76
Other values (213)
5366 

Length

Max length28
Median length16
Mean length15.97465517
Min length4

Characters and Unicode

Total characters92653
Distinct characters50
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCanada Goose
2nd rowCanada Goose
3rd rowCanada Goose
4th rowCanada Goose
5th rowCanada Goose

Common Values

ValueCountFrequency (%)
Song Sparrow95
 
1.6%
Blue Jay94
 
1.6%
Carolina Wren93
 
1.6%
Northern Cardinal76
 
1.3%
House Wren76
 
1.3%
Bewick's Wren72
 
1.2%
Common Yellowthroat69
 
1.2%
Red Crossbill69
 
1.2%
Spotted Towhee68
 
1.2%
Red-winged Blackbird64
 
1.1%
Other values (208)5024
86.6%

Length

2021-05-17T23:30:02.171878image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
warbler620
 
5.3%
sparrow573
 
4.9%
wren416
 
3.6%
american343
 
2.9%
northern297
 
2.5%
flycatcher245
 
2.1%
jay228
 
1.9%
towhee216
 
1.8%
woodpecker215
 
1.8%
vireo211
 
1.8%
Other values (233)8350
71.3%

Most occurring characters

ValueCountFrequency (%)
e10079
 
10.9%
r8954
 
9.7%
a6681
 
7.2%
5914
 
6.4%
o5655
 
6.1%
n4672
 
5.0%
i4464
 
4.8%
l4455
 
4.8%
t3793
 
4.1%
d3524
 
3.8%
Other values (40)34462
37.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter72904
78.7%
Uppercase Letter11749
 
12.7%
Space Separator5914
 
6.4%
Dash Punctuation1417
 
1.5%
Other Punctuation669
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e10079
13.8%
r8954
12.3%
a6681
 
9.2%
o5655
 
7.8%
n4672
 
6.4%
i4464
 
6.1%
l4455
 
6.1%
t3793
 
5.2%
d3524
 
4.8%
c2988
 
4.1%
Other values (14)17639
24.2%
Uppercase Letter
ValueCountFrequency (%)
W1854
15.8%
C1264
10.8%
S1230
 
10.5%
B1060
 
9.0%
T627
 
5.3%
G623
 
5.3%
R587
 
5.0%
A508
 
4.3%
H459
 
3.9%
F442
 
3.8%
Other values (13)3095
26.3%
Space Separator
ValueCountFrequency (%)
5914
100.0%
Other Punctuation
ValueCountFrequency (%)
'669
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1417
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin84653
91.4%
Common8000
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e10079
 
11.9%
r8954
 
10.6%
a6681
 
7.9%
o5655
 
6.7%
n4672
 
5.5%
i4464
 
5.3%
l4455
 
5.3%
t3793
 
4.5%
d3524
 
4.2%
c2988
 
3.5%
Other values (37)29388
34.7%
Common
ValueCountFrequency (%)
5914
73.9%
-1417
 
17.7%
'669
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII92653
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e10079
 
10.9%
r8954
 
9.7%
a6681
 
7.2%
5914
 
6.4%
o5655
 
6.1%
n4672
 
5.0%
i4464
 
4.8%
l4455
 
4.8%
t3793
 
4.1%
d3524
 
3.8%
Other values (40)34462
37.2%

rec
Categorical

HIGH CARDINALITY

Distinct394
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size45.4 KiB
Paul Marvin
500 
Mike Nelson
470 
Sue Riffe
 
229
Richard E. Webster
 
219
Antonio Xeira
 
207
Other values (389)
4175 

Length

Max length40
Median length12
Mean length12.64396552
Min length3

Characters and Unicode

Total characters73335
Distinct characters66
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique160 ?
Unique (%)2.8%

Sample

1st rowBruce Lagerquist
2nd rowSue Riffe
3rd rowEric Hough
4th rowGarrett MacDonald
5th rowAlbert @ Max lastukhin

Common Values

ValueCountFrequency (%)
Paul Marvin500
 
8.6%
Mike Nelson470
 
8.1%
Sue Riffe229
 
3.9%
Richard E. Webster219
 
3.8%
Antonio Xeira207
 
3.6%
Bobby Wilcox185
 
3.2%
Ted Floyd178
 
3.1%
Peter Boesman166
 
2.9%
Andrew Spencer161
 
2.8%
Thomas Magarian126
 
2.2%
Other values (384)3359
57.9%

Length

2021-05-17T23:30:02.741405image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
paul617
 
5.0%
marvin500
 
4.0%
mike482
 
3.9%
nelson472
 
3.8%
richard274
 
2.2%
e263
 
2.1%
webster260
 
2.1%
thomas245
 
2.0%
riffe229
 
1.9%
sue229
 
1.9%
Other values (601)8797
71.1%

Most occurring characters

ValueCountFrequency (%)
e6927
 
9.4%
6574
 
9.0%
a5922
 
8.1%
r5167
 
7.0%
n4685
 
6.4%
i4640
 
6.3%
o4070
 
5.5%
l3032
 
4.1%
s2782
 
3.8%
t2310
 
3.1%
Other values (56)27226
37.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter53321
72.7%
Uppercase Letter12673
 
17.3%
Space Separator6574
 
9.0%
Other Punctuation733
 
1.0%
Dash Punctuation31
 
< 0.1%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%
Decimal Number1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M1450
 
11.4%
P1126
 
8.9%
B877
 
6.9%
R849
 
6.7%
S793
 
6.3%
D744
 
5.9%
W740
 
5.8%
T727
 
5.7%
N678
 
5.3%
A656
 
5.2%
Other values (18)4033
31.8%
Lowercase Letter
ValueCountFrequency (%)
e6927
13.0%
a5922
11.1%
r5167
9.7%
n4685
 
8.8%
i4640
 
8.7%
o4070
 
7.6%
l3032
 
5.7%
s2782
 
5.2%
t2310
 
4.3%
d1621
 
3.0%
Other values (18)12165
22.8%
Other Punctuation
ValueCountFrequency (%)
.672
91.7%
'38
 
5.2%
&19
 
2.6%
,3
 
0.4%
@1
 
0.1%
Space Separator
ValueCountFrequency (%)
6574
100.0%
Dash Punctuation
ValueCountFrequency (%)
-31
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%
Decimal Number
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin65994
90.0%
Common7341
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e6927
 
10.5%
a5922
 
9.0%
r5167
 
7.8%
n4685
 
7.1%
i4640
 
7.0%
o4070
 
6.2%
l3032
 
4.6%
s2782
 
4.2%
t2310
 
3.5%
d1621
 
2.5%
Other values (46)24838
37.6%
Common
ValueCountFrequency (%)
6574
89.6%
.672
 
9.2%
'38
 
0.5%
-31
 
0.4%
&19
 
0.3%
,3
 
< 0.1%
@1
 
< 0.1%
(1
 
< 0.1%
)1
 
< 0.1%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII73321
> 99.9%
Latin 1 Sup14
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e6927
 
9.4%
6574
 
9.0%
a5922
 
8.1%
r5167
 
7.0%
n4685
 
6.4%
i4640
 
6.3%
o4070
 
5.6%
l3032
 
4.1%
s2782
 
3.8%
t2310
 
3.2%
Other values (52)27212
37.1%
Latin 1 Sup
ValueCountFrequency (%)
Å6
42.9%
É6
42.9%
é1
 
7.1%
ø1
 
7.1%

cnt
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.4 KiB
United States
5800 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters75400
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited States
2nd rowUnited States
3rd rowUnited States
4th rowUnited States
5th rowUnited States

Common Values

ValueCountFrequency (%)
United States5800
100.0%

Length

2021-05-17T23:30:03.058124image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-17T23:30:03.167316image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
states5800
50.0%
united5800
50.0%

Most occurring characters

ValueCountFrequency (%)
t17400
23.1%
e11600
15.4%
U5800
 
7.7%
n5800
 
7.7%
i5800
 
7.7%
d5800
 
7.7%
5800
 
7.7%
S5800
 
7.7%
a5800
 
7.7%
s5800
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter58000
76.9%
Uppercase Letter11600
 
15.4%
Space Separator5800
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t17400
30.0%
e11600
20.0%
n5800
 
10.0%
i5800
 
10.0%
d5800
 
10.0%
a5800
 
10.0%
s5800
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
U5800
50.0%
S5800
50.0%
Space Separator
ValueCountFrequency (%)
5800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin69600
92.3%
Common5800
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t17400
25.0%
e11600
16.7%
U5800
 
8.3%
n5800
 
8.3%
i5800
 
8.3%
d5800
 
8.3%
S5800
 
8.3%
a5800
 
8.3%
s5800
 
8.3%
Common
ValueCountFrequency (%)
5800
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII75400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t17400
23.1%
e11600
15.4%
U5800
 
7.7%
n5800
 
7.7%
i5800
 
7.7%
d5800
 
7.7%
5800
 
7.7%
S5800
 
7.7%
a5800
 
7.7%
s5800
 
7.7%

loc
Categorical

HIGH CARDINALITY

Distinct2456
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Memory size45.4 KiB
Portal, Arizona
 
65
Knoxville, Tennessee
 
50
Hendrix Habitat - Fairview, Williamson County, Tennessee
 
43
Schoolhouse Gap Trail, Great Smoky Mountains National Park, Tennessee
 
43
Tama (near Burlington), Des Moines, Iowa
 
31
Other values (2451)
5568 

Length

Max length120
Median length43
Mean length44.51482759
Min length9

Characters and Unicode

Total characters258186
Distinct characters79
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1432 ?
Unique (%)24.7%

Sample

1st rowSedro-Woolley, Skagit County, Washington
2nd rowAu Sable SF - Big Creek Rd, Michigan
3rd rowSan Juan River, Cottonwood Day-Use Area, Navajo Lake State Park, San Juan County, New Mexico
4th rowBeluga--North Bog, Kenai Peninsula Borough, Alaska
5th rowOyster Bay (near Lattingtown), Nassau, New York

Common Values

ValueCountFrequency (%)
Portal, Arizona65
 
1.1%
Knoxville, Tennessee50
 
0.9%
Hendrix Habitat - Fairview, Williamson County, Tennessee43
 
0.7%
Schoolhouse Gap Trail, Great Smoky Mountains National Park, Tennessee43
 
0.7%
Tama (near Burlington), Des Moines, Iowa31
 
0.5%
Viera Wetlands, Florida29
 
0.5%
San Bernardino National Wildlife Refuge, Cochise County, Arizona28
 
0.5%
Manomet Bird Observatory, Manomet, Plymouth County, Massachusetts27
 
0.5%
Oxford, Lafayette County, Mississippi26
 
0.4%
Peña Blanca Lake, Arizona25
 
0.4%
Other values (2446)5433
93.7%

Length

2021-05-17T23:30:03.575588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
county3212
 
9.0%
park829
 
2.3%
arizona790
 
2.2%
california747
 
2.1%
near617
 
1.7%
national530
 
1.5%
colorado499
 
1.4%
co493
 
1.4%
san488
 
1.4%
tennessee389
 
1.1%
Other values (3090)26999
75.9%

Most occurring characters

ValueCountFrequency (%)
30419
 
11.8%
a22883
 
8.9%
o18815
 
7.3%
n18641
 
7.2%
e17807
 
6.9%
r13914
 
5.4%
i13467
 
5.2%
,11783
 
4.6%
t11649
 
4.5%
l9611
 
3.7%
Other values (69)89197
34.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter177395
68.7%
Uppercase Letter35849
 
13.9%
Space Separator30419
 
11.8%
Other Punctuation12633
 
4.9%
Open Punctuation607
 
0.2%
Close Punctuation607
 
0.2%
Decimal Number425
 
0.2%
Dash Punctuation250
 
0.1%
Control1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a22883
12.9%
o18815
10.6%
n18641
10.5%
e17807
10.0%
r13914
 
7.8%
i13467
 
7.6%
t11649
 
6.6%
l9611
 
5.4%
s8677
 
4.9%
u7142
 
4.0%
Other values (18)34789
19.6%
Uppercase Letter
ValueCountFrequency (%)
C8013
22.4%
S2778
 
7.7%
M2703
 
7.5%
P2661
 
7.4%
A2183
 
6.1%
N2086
 
5.8%
R1913
 
5.3%
B1696
 
4.7%
W1522
 
4.2%
T1493
 
4.2%
Other values (16)8801
24.6%
Other Punctuation
ValueCountFrequency (%)
,11783
93.3%
.615
 
4.9%
'141
 
1.1%
/34
 
0.3%
&26
 
0.2%
:16
 
0.1%
"10
 
0.1%
;4
 
< 0.1%
#3
 
< 0.1%
@1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1103
24.2%
268
16.0%
450
11.8%
049
11.5%
337
 
8.7%
532
 
7.5%
732
 
7.5%
629
 
6.8%
815
 
3.5%
910
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
-250
100.0%
Space Separator
ValueCountFrequency (%)
30419
100.0%
Open Punctuation
ValueCountFrequency (%)
(607
100.0%
Close Punctuation
ValueCountFrequency (%)
)607
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin213244
82.6%
Common44942
 
17.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a22883
 
10.7%
o18815
 
8.8%
n18641
 
8.7%
e17807
 
8.4%
r13914
 
6.5%
i13467
 
6.3%
t11649
 
5.5%
l9611
 
4.5%
s8677
 
4.1%
C8013
 
3.8%
Other values (44)69767
32.7%
Common
ValueCountFrequency (%)
30419
67.7%
,11783
 
26.2%
.615
 
1.4%
(607
 
1.4%
)607
 
1.4%
-250
 
0.6%
'141
 
0.3%
1103
 
0.2%
268
 
0.2%
450
 
0.1%
Other values (15)299
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII258153
> 99.9%
Latin 1 Sup33
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
30419
 
11.8%
a22883
 
8.9%
o18815
 
7.3%
n18641
 
7.2%
e17807
 
6.9%
r13914
 
5.4%
i13467
 
5.2%
,11783
 
4.6%
t11649
 
4.5%
l9611
 
3.7%
Other values (67)89164
34.5%
Latin 1 Sup
ValueCountFrequency (%)
ñ32
97.0%
í1
 
3.0%

lat
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct2577
Distinct (%)45.1%
Missing90
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean37.60166006
Minimum19.934
Maximum68.6315
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.4 KiB
2021-05-17T23:30:03.788844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum19.934
5-th percentile29.57237
Q132.9808
median36.4836
Q340.8918
95-th percentile47.374625
Maximum68.6315
Range48.6975
Interquartile range (IQR)7.911

Descriptive statistics

Standard deviation6.041136133
Coefficient of variation (CV)0.1606614209
Kurtosis3.42527558
Mean37.60166006
Median Absolute Deviation (MAD)3.98805
Skewness1.183392668
Sum214705.479
Variance36.49532577
MonotonicityNot monotonic
2021-05-17T23:30:04.003204image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.90659
 
1.0%
35.858950
 
0.9%
35.98743
 
0.7%
35.627443
 
0.7%
40.891831
 
0.5%
28.226729
 
0.5%
31.3428
 
0.5%
41.9227
 
0.5%
31.425
 
0.4%
36.000824
 
0.4%
Other values (2567)5351
92.3%
(Missing)90
 
1.6%
ValueCountFrequency (%)
19.9341
 
< 0.1%
21.57481
 
< 0.1%
24.54581
 
< 0.1%
24.6032
 
< 0.1%
24.6963
0.1%
24.69895
0.1%
25.1383
0.1%
25.17411
 
< 0.1%
25.21381
 
< 0.1%
25.38932
 
< 0.1%
ValueCountFrequency (%)
68.63151
 
< 0.1%
68.37481
 
< 0.1%
68.24131
 
< 0.1%
65.12631
 
< 0.1%
64.91616
0.1%
64.86324
0.1%
64.86261
 
< 0.1%
64.79041
 
< 0.1%
64.63051
 
< 0.1%
64.56224
0.1%

lng
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct2577
Distinct (%)45.1%
Missing90
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean-99.77350236
Minimum-170.2853394
Maximum-67.1484
Zeros0
Zeros (%)0.0%
Negative5710
Negative (%)98.4%
Memory size45.4 KiB
2021-05-17T23:30:04.226818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-170.2853394
5-th percentile-122.316025
Q1-114.0401
median-104.46435
Q3-83.7265
95-th percentile-73.6537
Maximum-67.1484
Range103.1369394
Interquartile range (IQR)30.3136

Descriptive statistics

Standard deviation17.81190747
Coefficient of variation (CV)-0.178523426
Kurtosis-0.2598406514
Mean-99.77350236
Median Absolute Deviation (MAD)15.31085
Skewness-0.2307219282
Sum-569706.6985
Variance317.2640477
MonotonicityNot monotonic
2021-05-17T23:30:04.431467image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-109.154359
 
1.0%
-84.094850
 
0.9%
-87.14543
 
0.7%
-83.726543
 
0.7%
-91.075631
 
0.5%
-80.764529
 
0.5%
-109.27328
 
0.5%
-70.54327
 
0.5%
-111.08825
 
0.4%
-83.946224
 
0.4%
Other values (2567)5351
92.3%
(Missing)90
 
1.6%
ValueCountFrequency (%)
-170.28533941
 
< 0.1%
-165.4934
 
0.1%
-165.47181
 
< 0.1%
-165.42491
 
< 0.1%
-165.405310
0.2%
-165.30551
 
< 0.1%
-165.21151
 
< 0.1%
-164.9646
0.1%
-164.72931
 
< 0.1%
-158.27971
 
< 0.1%
ValueCountFrequency (%)
-67.14841
 
< 0.1%
-67.19442
< 0.1%
-67.85811
 
< 0.1%
-67.86123
0.1%
-68.21171
 
< 0.1%
-68.22481
 
< 0.1%
-68.3122
< 0.1%
-68.3751
 
< 0.1%
-68.60091
 
< 0.1%
-68.7291
 
< 0.1%

alt
Categorical

HIGH CARDINALITY
MISSING

Distinct245
Distinct (%)4.3%
Missing113
Missing (%)1.9%
Memory size45.4 KiB
0
 
385
10
 
281
?
 
260
1600
 
190
20
 
173
Other values (240)
4398 

Length

Max length11
Median length3
Mean length2.893617021
Min length1

Characters and Unicode

Total characters16456
Distinct characters17
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)1.8%

Sample

1st row30
2nd row180
3rd row1800
4th row40
5th row10

Common Values

ValueCountFrequency (%)
0385
 
6.6%
10281
 
4.8%
?260
 
4.5%
1600190
 
3.3%
20173
 
3.0%
300168
 
2.9%
1500152
 
2.6%
50148
 
2.6%
1100148
 
2.6%
1400144
 
2.5%
Other values (235)3638
62.7%

Length

2021-05-17T23:30:04.919667image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0398
 
6.9%
10281
 
4.9%
279
 
4.9%
1600190
 
3.3%
20173
 
3.0%
300168
 
2.9%
50158
 
2.8%
1500152
 
2.7%
1400150
 
2.6%
1100148
 
2.6%
Other values (227)3634
63.4%

Most occurring characters

ValueCountFrequency (%)
07669
46.6%
12436
 
14.8%
21687
 
10.3%
51005
 
6.1%
4692
 
4.2%
3688
 
4.2%
6639
 
3.9%
8497
 
3.0%
7474
 
2.9%
9296
 
1.8%
Other values (7)373
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number16083
97.7%
Other Punctuation274
 
1.7%
Dash Punctuation49
 
0.3%
Space Separator44
 
0.3%
Lowercase Letter6
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07669
47.7%
12436
 
15.1%
21687
 
10.5%
51005
 
6.2%
4692
 
4.3%
3688
 
4.3%
6639
 
4.0%
8497
 
3.1%
7474
 
2.9%
9296
 
1.8%
Other Punctuation
ValueCountFrequency (%)
?260
94.9%
,8
 
2.9%
.5
 
1.8%
:1
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
-49
100.0%
Space Separator
ValueCountFrequency (%)
44
100.0%
Lowercase Letter
ValueCountFrequency (%)
m6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common16450
> 99.9%
Latin6
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
07669
46.6%
12436
 
14.8%
21687
 
10.3%
51005
 
6.1%
4692
 
4.2%
3688
 
4.2%
6639
 
3.9%
8497
 
3.0%
7474
 
2.9%
9296
 
1.8%
Other values (6)367
 
2.2%
Latin
ValueCountFrequency (%)
m6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII16456
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
07669
46.6%
12436
 
14.8%
21687
 
10.3%
51005
 
6.1%
4692
 
4.2%
3688
 
4.2%
6639
 
3.9%
8497
 
3.0%
7474
 
2.9%
9296
 
1.8%
Other values (7)373
 
2.3%

type
Categorical

HIGH CARDINALITY

Distinct276
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size45.4 KiB
call
2393 
song
2016 
Song
 
173
male, song
 
127
adult, male, song
 
111
Other values (271)
980 

Length

Max length65
Median length4
Mean length7.007931034
Min length4

Characters and Unicode

Total characters40646
Distinct characters61
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique205 ?
Unique (%)3.5%

Sample

1st rowcall
2nd rowsong
3rd rowcall
4th rowcall, flight call
5th rowcall

Common Values

ValueCountFrequency (%)
call2393
41.3%
song2016
34.8%
Song173
 
3.0%
male, song127
 
2.2%
adult, male, song111
 
1.9%
Call108
 
1.9%
adult, call, sex uncertain106
 
1.8%
adult, sex uncertain, song58
 
1.0%
call, flight call55
 
0.9%
call, male52
 
0.9%
Other values (266)601
 
10.4%

Length

2021-05-17T23:30:05.367168image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
call3363
40.8%
song2643
32.1%
adult402
 
4.9%
male367
 
4.5%
uncertain328
 
4.0%
sex275
 
3.3%
flight98
 
1.2%
female89
 
1.1%
juvenile53
 
0.6%
stage53
 
0.6%
Other values (219)562
 
6.8%

Most occurring characters

ValueCountFrequency (%)
l7984
19.6%
a4878
12.0%
c3713
9.1%
n3592
8.8%
s2933
 
7.2%
g2929
 
7.2%
o2780
 
6.8%
2434
 
6.0%
,1678
 
4.1%
e1634
 
4.0%
Other values (51)6091
15.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter36034
88.7%
Space Separator2434
 
6.0%
Other Punctuation1750
 
4.3%
Uppercase Letter404
 
1.0%
Dash Punctuation16
 
< 0.1%
Decimal Number4
 
< 0.1%
Open Punctuation2
 
< 0.1%
Close Punctuation2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l7984
22.2%
a4878
13.5%
c3713
10.3%
n3592
10.0%
s2933
 
8.1%
g2929
 
8.1%
o2780
 
7.7%
e1634
 
4.5%
t1138
 
3.2%
i860
 
2.4%
Other values (16)3593
10.0%
Uppercase Letter
ValueCountFrequency (%)
S190
47.0%
C139
34.4%
P12
 
3.0%
W12
 
3.0%
R8
 
2.0%
G5
 
1.2%
K4
 
1.0%
T4
 
1.0%
A4
 
1.0%
D4
 
1.0%
Other values (12)22
 
5.4%
Other Punctuation
ValueCountFrequency (%)
,1678
95.9%
"41
 
2.3%
/20
 
1.1%
.5
 
0.3%
'3
 
0.2%
?2
 
0.1%
:1
 
0.1%
Decimal Number
ValueCountFrequency (%)
13
75.0%
21
 
25.0%
Space Separator
ValueCountFrequency (%)
2434
100.0%
Dash Punctuation
ValueCountFrequency (%)
-16
100.0%
Open Punctuation
ValueCountFrequency (%)
(2
100.0%
Close Punctuation
ValueCountFrequency (%)
)2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin36438
89.6%
Common4208
 
10.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
l7984
21.9%
a4878
13.4%
c3713
10.2%
n3592
9.9%
s2933
 
8.0%
g2929
 
8.0%
o2780
 
7.6%
e1634
 
4.5%
t1138
 
3.1%
i860
 
2.4%
Other values (38)3997
11.0%
Common
ValueCountFrequency (%)
2434
57.8%
,1678
39.9%
"41
 
1.0%
/20
 
0.5%
-16
 
0.4%
.5
 
0.1%
'3
 
0.1%
13
 
0.1%
(2
 
< 0.1%
)2
 
< 0.1%
Other values (3)4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII40646
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l7984
19.6%
a4878
12.0%
c3713
9.1%
n3592
8.8%
s2933
 
7.2%
g2929
 
7.2%
o2780
 
6.8%
2434
 
6.0%
,1678
 
4.1%
e1634
 
4.0%
Other values (51)6091
15.0%

url
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct5800
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size45.4 KiB
//www.xeno-canto.org/165298
 
1
//www.xeno-canto.org/504450
 
1
//www.xeno-canto.org/580703
 
1
//www.xeno-canto.org/103947
 
1
//www.xeno-canto.org/324402
 
1
Other values (5795)
5795 

Length

Max length27
Median length27
Mean length26.81482759
Min length25

Characters and Unicode

Total characters155526
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5800 ?
Unique (%)100.0%

Sample

1st row//www.xeno-canto.org/454911
2nd row//www.xeno-canto.org/418340
3rd row//www.xeno-canto.org/291051
4th row//www.xeno-canto.org/283618
5th row//www.xeno-canto.org/209702

Common Values

ValueCountFrequency (%)
//www.xeno-canto.org/1652981
 
< 0.1%
//www.xeno-canto.org/5044501
 
< 0.1%
//www.xeno-canto.org/5807031
 
< 0.1%
//www.xeno-canto.org/1039471
 
< 0.1%
//www.xeno-canto.org/3244021
 
< 0.1%
//www.xeno-canto.org/1047101
 
< 0.1%
//www.xeno-canto.org/777871
 
< 0.1%
//www.xeno-canto.org/5509501
 
< 0.1%
//www.xeno-canto.org/2772981
 
< 0.1%
//www.xeno-canto.org/2679701
 
< 0.1%
Other values (5790)5790
99.8%

Length

2021-05-17T23:30:05.724287image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
www.xeno-canto.org/5728141
 
< 0.1%
www.xeno-canto.org/6028271
 
< 0.1%
www.xeno-canto.org/4638671
 
< 0.1%
www.xeno-canto.org/1092681
 
< 0.1%
www.xeno-canto.org/1354761
 
< 0.1%
www.xeno-canto.org/3847041
 
< 0.1%
www.xeno-canto.org/2959421
 
< 0.1%
www.xeno-canto.org/4902001
 
< 0.1%
www.xeno-canto.org/3886821
 
< 0.1%
www.xeno-canto.org/2890061
 
< 0.1%
Other values (5790)5790
99.8%

Most occurring characters

ValueCountFrequency (%)
/17400
 
11.2%
w17400
 
11.2%
o17400
 
11.2%
.11600
 
7.5%
n11600
 
7.5%
x5800
 
3.7%
e5800
 
3.7%
-5800
 
3.7%
c5800
 
3.7%
a5800
 
3.7%
Other values (13)51126
32.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter87000
55.9%
Decimal Number33726
 
21.7%
Other Punctuation29000
 
18.6%
Dash Punctuation5800
 
3.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w17400
20.0%
o17400
20.0%
n11600
13.3%
x5800
 
6.7%
e5800
 
6.7%
c5800
 
6.7%
a5800
 
6.7%
t5800
 
6.7%
r5800
 
6.7%
g5800
 
6.7%
Decimal Number
ValueCountFrequency (%)
14567
13.5%
34102
12.2%
23755
11.1%
53542
10.5%
43535
10.5%
02970
8.8%
72936
8.7%
62857
8.5%
82744
8.1%
92718
8.1%
Other Punctuation
ValueCountFrequency (%)
/17400
60.0%
.11600
40.0%
Dash Punctuation
ValueCountFrequency (%)
-5800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin87000
55.9%
Common68526
44.1%

Most frequent character per script

Common
ValueCountFrequency (%)
/17400
25.4%
.11600
16.9%
-5800
 
8.5%
14567
 
6.7%
34102
 
6.0%
23755
 
5.5%
53542
 
5.2%
43535
 
5.2%
02970
 
4.3%
72936
 
4.3%
Other values (3)8319
12.1%
Latin
ValueCountFrequency (%)
w17400
20.0%
o17400
20.0%
n11600
13.3%
x5800
 
6.7%
e5800
 
6.7%
c5800
 
6.7%
a5800
 
6.7%
t5800
 
6.7%
r5800
 
6.7%
g5800
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII155526
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/17400
 
11.2%
w17400
 
11.2%
o17400
 
11.2%
.11600
 
7.5%
n11600
 
7.5%
x5800
 
3.7%
e5800
 
3.7%
-5800
 
3.7%
c5800
 
3.7%
a5800
 
3.7%
Other values (13)51126
32.9%

file
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct5800
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size45.4 KiB
//www.xeno-canto.org/101362/download
 
1
//www.xeno-canto.org/126629/download
 
1
//www.xeno-canto.org/103260/download
 
1
//www.xeno-canto.org/287314/download
 
1
//www.xeno-canto.org/413193/download
 
1
Other values (5795)
5795 

Length

Max length36
Median length36
Mean length35.81482759
Min length34

Characters and Unicode

Total characters207726
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5800 ?
Unique (%)100.0%

Sample

1st row//www.xeno-canto.org/454911/download
2nd row//www.xeno-canto.org/418340/download
3rd row//www.xeno-canto.org/291051/download
4th row//www.xeno-canto.org/283618/download
5th row//www.xeno-canto.org/209702/download

Common Values

ValueCountFrequency (%)
//www.xeno-canto.org/101362/download1
 
< 0.1%
//www.xeno-canto.org/126629/download1
 
< 0.1%
//www.xeno-canto.org/103260/download1
 
< 0.1%
//www.xeno-canto.org/287314/download1
 
< 0.1%
//www.xeno-canto.org/413193/download1
 
< 0.1%
//www.xeno-canto.org/218167/download1
 
< 0.1%
//www.xeno-canto.org/177057/download1
 
< 0.1%
//www.xeno-canto.org/297468/download1
 
< 0.1%
//www.xeno-canto.org/435682/download1
 
< 0.1%
//www.xeno-canto.org/79575/download1
 
< 0.1%
Other values (5790)5790
99.8%

Length

2021-05-17T23:30:06.334462image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
www.xeno-canto.org/369841/download1
 
< 0.1%
www.xeno-canto.org/103283/download1
 
< 0.1%
www.xeno-canto.org/53721/download1
 
< 0.1%
www.xeno-canto.org/233191/download1
 
< 0.1%
www.xeno-canto.org/384704/download1
 
< 0.1%
www.xeno-canto.org/35891/download1
 
< 0.1%
www.xeno-canto.org/313539/download1
 
< 0.1%
www.xeno-canto.org/265366/download1
 
< 0.1%
www.xeno-canto.org/421763/download1
 
< 0.1%
www.xeno-canto.org/476090/download1
 
< 0.1%
Other values (5790)5790
99.8%

Most occurring characters

ValueCountFrequency (%)
o29000
14.0%
/23200
 
11.2%
w23200
 
11.2%
n17400
 
8.4%
.11600
 
5.6%
a11600
 
5.6%
d11600
 
5.6%
x5800
 
2.8%
e5800
 
2.8%
-5800
 
2.8%
Other values (15)62726
30.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter133400
64.2%
Other Punctuation34800
 
16.8%
Decimal Number33726
 
16.2%
Dash Punctuation5800
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o29000
21.7%
w23200
17.4%
n17400
13.0%
a11600
 
8.7%
d11600
 
8.7%
x5800
 
4.3%
e5800
 
4.3%
c5800
 
4.3%
t5800
 
4.3%
r5800
 
4.3%
Other values (2)11600
 
8.7%
Decimal Number
ValueCountFrequency (%)
14567
13.5%
34102
12.2%
23755
11.1%
53542
10.5%
43535
10.5%
02970
8.8%
72936
8.7%
62857
8.5%
82744
8.1%
92718
8.1%
Other Punctuation
ValueCountFrequency (%)
/23200
66.7%
.11600
33.3%
Dash Punctuation
ValueCountFrequency (%)
-5800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin133400
64.2%
Common74326
35.8%

Most frequent character per script

Common
ValueCountFrequency (%)
/23200
31.2%
.11600
15.6%
-5800
 
7.8%
14567
 
6.1%
34102
 
5.5%
23755
 
5.1%
53542
 
4.8%
43535
 
4.8%
02970
 
4.0%
72936
 
4.0%
Other values (3)8319
 
11.2%
Latin
ValueCountFrequency (%)
o29000
21.7%
w23200
17.4%
n17400
13.0%
a11600
 
8.7%
d11600
 
8.7%
x5800
 
4.3%
e5800
 
4.3%
c5800
 
4.3%
t5800
 
4.3%
r5800
 
4.3%
Other values (2)11600
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII207726
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o29000
14.0%
/23200
 
11.2%
w23200
 
11.2%
n17400
 
8.4%
.11600
 
5.6%
a11600
 
5.6%
d11600
 
5.6%
x5800
 
2.8%
e5800
 
2.8%
-5800
 
2.8%
Other values (15)62726
30.2%

file-name
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct5800
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size45.4 KiB
XC361018-160510_0654 0800 Kern Adams_NOCA.mp3
 
1
XC573456-EasternMeadowlarkJul4_2.mp3
 
1
XC377068-DSCN8831.mp3
 
1
XC165277-American Robin -CA, Mt Laguna,, December 03, ‎2012, 0659 AM.mp3
 
1
XC325013-Brewer_39_s_Sparrow_2001.mp3
 
1
Other values (5795)
5795 

Length

Max length158
Median length44
Mean length46.90827586
Min length7

Characters and Unicode

Total characters272068
Distinct characters108
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5800 ?
Unique (%)100.0%

Sample

1st rowXC454911-190202_02 Canadian Geese.mp3
2nd rowXC418340-Canada Goose on 5.11.18 at Au Sable SF MI at 11.20 for .14 _0908 .mp3
3rd rowXC291051-CANG_11515_1730_SanJuanRiver-NavajoDam.mp3
4th rowXC283618-LS100466.mp3
5th rowXC209702-Poecile atricapillus Dec_27,_2014,_4_05_PM,C1.mp3

Common Values

ValueCountFrequency (%)
XC361018-160510_0654 0800 Kern Adams_NOCA.mp31
 
< 0.1%
XC573456-EasternMeadowlarkJul4_2.mp31
 
< 0.1%
XC377068-DSCN8831.mp31
 
< 0.1%
XC165277-American Robin -CA, Mt Laguna,, December 03, ‎2012, 0659 AM.mp31
 
< 0.1%
XC325013-Brewer_39_s_Sparrow_2001.mp31
 
< 0.1%
XC445965-Hairy Woodpecker.mp31
 
< 0.1%
XC135716-Winter Wren1304.mp31
 
< 0.1%
XC514107-LOSH.mp31
 
< 0.1%
XC574232-Virginia Rail -CA, Proctor Valley Rd, July 06, 2020, 0548 AM.mp31
 
< 0.1%
XC389422-COLO WTSP call wiwa blja amro cang 1800 10032017 spl NK for xc.mp31
 
< 0.1%
Other values (5790)5790
99.8%

Length

2021-05-17T23:30:06.907583image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
am.mp3387
 
1.6%
at308
 
1.3%
219
 
0.9%
ca218
 
0.9%
mp3186
 
0.8%
amp.mp3173
 
0.7%
call167
 
0.7%
on165
 
0.7%
for165
 
0.7%
song159
 
0.6%
Other values (11112)22382
91.2%

Most occurring characters

ValueCountFrequency (%)
18822
 
6.9%
014537
 
5.3%
112926
 
4.8%
312316
 
4.5%
a10555
 
3.9%
e10260
 
3.8%
210066
 
3.7%
-9967
 
3.7%
r8785
 
3.2%
m8119
 
3.0%
Other values (98)155715
57.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter103261
38.0%
Decimal Number79903
29.4%
Uppercase Letter43151
15.9%
Space Separator18822
 
6.9%
Other Punctuation10307
 
3.8%
Dash Punctuation9967
 
3.7%
Connector Punctuation6032
 
2.2%
Close Punctuation262
 
0.1%
Open Punctuation261
 
0.1%
Format94
 
< 0.1%
Other values (3)8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a10555
 
10.2%
e10260
 
9.9%
r8785
 
8.5%
m8119
 
7.9%
p7924
 
7.7%
o7262
 
7.0%
n6614
 
6.4%
l6262
 
6.1%
i5466
 
5.3%
t4937
 
4.8%
Other values (37)27077
26.2%
Uppercase Letter
ValueCountFrequency (%)
C7799
18.1%
X4687
 
10.9%
A2962
 
6.9%
S2684
 
6.2%
M2471
 
5.7%
R2270
 
5.3%
W2201
 
5.1%
P1986
 
4.6%
B1818
 
4.2%
L1592
 
3.7%
Other values (20)12681
29.4%
Decimal Number
ValueCountFrequency (%)
014537
18.2%
112926
16.2%
312316
15.4%
210066
12.6%
56188
7.7%
45550
 
6.9%
64928
 
6.2%
74549
 
5.7%
84480
 
5.6%
94363
 
5.5%
Other Punctuation
ValueCountFrequency (%)
.7445
72.2%
,2572
 
25.0%
'261
 
2.5%
&12
 
0.1%
@7
 
0.1%
:4
 
< 0.1%
#3
 
< 0.1%
?2
 
< 0.1%
;1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
(250
95.8%
[11
 
4.2%
Close Punctuation
ValueCountFrequency (%)
)251
95.8%
]11
 
4.2%
Format
ValueCountFrequency (%)
93
98.9%
1
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
-9967
100.0%
Connector Punctuation
ValueCountFrequency (%)
_6032
100.0%
Space Separator
ValueCountFrequency (%)
18822
100.0%
Math Symbol
ValueCountFrequency (%)
+6
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%
Nonspacing Mark
ValueCountFrequency (%)
̀1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin146305
53.8%
Common125655
46.2%
Cyrillic107
 
< 0.1%
Inherited1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a10555
 
7.2%
e10260
 
7.0%
r8785
 
6.0%
m8119
 
5.5%
p7924
 
5.4%
C7799
 
5.3%
o7262
 
5.0%
n6614
 
4.5%
l6262
 
4.3%
i5466
 
3.7%
Other values (51)67259
46.0%
Common
ValueCountFrequency (%)
18822
15.0%
014537
11.6%
112926
10.3%
312316
9.8%
210066
8.0%
-9967
7.9%
.7445
 
5.9%
56188
 
4.9%
_6032
 
4.8%
45550
 
4.4%
Other values (20)21806
17.4%
Cyrillic
ValueCountFrequency (%)
р22
20.6%
и20
18.7%
М12
11.2%
л10
9.3%
в10
9.3%
е10
9.3%
А4
 
3.7%
а4
 
3.7%
я4
 
3.7%
к3
 
2.8%
Other values (6)8
 
7.5%
Inherited
ValueCountFrequency (%)
̀1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII271841
99.9%
Cyrillic107
 
< 0.1%
Punctuation95
 
< 0.1%
Latin 1 Sup23
 
< 0.1%
Diacriticals1
 
< 0.1%
Latin Ext B1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18822
 
6.9%
014537
 
5.3%
112926
 
4.8%
312316
 
4.5%
a10555
 
3.9%
e10260
 
3.8%
210066
 
3.7%
-9967
 
3.7%
r8785
 
3.2%
m8119
 
3.0%
Other values (69)155488
57.2%
Latin 1 Sup
ValueCountFrequency (%)
å5
21.7%
è5
21.7%
ä4
17.4%
é4
17.4%
ö2
 
8.7%
à1
 
4.3%
ñ1
 
4.3%
Ì1
 
4.3%
Punctuation
ValueCountFrequency (%)
93
97.9%
1
 
1.1%
1
 
1.1%
Cyrillic
ValueCountFrequency (%)
р22
20.6%
и20
18.7%
М12
11.2%
л10
9.3%
в10
9.3%
е10
9.3%
А4
 
3.7%
а4
 
3.7%
я4
 
3.7%
к3
 
2.8%
Other values (6)8
 
7.5%
Diacriticals
ValueCountFrequency (%)
̀1
100.0%
Latin Ext B
ValueCountFrequency (%)
ƒ1
100.0%

sono
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct5800
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size45.4 KiB
{'small': '//www.xeno-canto.org/sounds/uploaded/YTUXOCTUEM/ffts/XC409005-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/YTUXOCTUEM/ffts/XC409005-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/YTUXOCTUEM/ffts/XC409005-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/YTUXOCTUEM/ffts/XC409005-full.png'}
 
1
{'small': '//www.xeno-canto.org/sounds/uploaded/OOECIWCSWV/ffts/XC219410-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/OOECIWCSWV/ffts/XC219410-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/OOECIWCSWV/ffts/XC219410-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/OOECIWCSWV/ffts/XC219410-full.png'}
 
1
{'small': '//www.xeno-canto.org/sounds/uploaded/PVQOLRXXWL/ffts/XC331991-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/PVQOLRXXWL/ffts/XC331991-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/PVQOLRXXWL/ffts/XC331991-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/PVQOLRXXWL/ffts/XC331991-full.png'}
 
1
{'small': '//www.xeno-canto.org/sounds/uploaded/OOECIWCSWV/ffts/XC220237-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/OOECIWCSWV/ffts/XC220237-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/OOECIWCSWV/ffts/XC220237-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/OOECIWCSWV/ffts/XC220237-full.png'}
 
1
{'small': '//www.xeno-canto.org/sounds/uploaded/ZQJCLMBULK/ffts/XC555363-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/ZQJCLMBULK/ffts/XC555363-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/ZQJCLMBULK/ffts/XC555363-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/ZQJCLMBULK/ffts/XC555363-full.png'}
 
1
Other values (5795)
5795 

Length

Max length330
Median length330
Mean length329.2593103
Min length322

Characters and Unicode

Total characters1909704
Distinct characters62
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5800 ?
Unique (%)100.0%

Sample

1st row{'small': '//www.xeno-canto.org/sounds/uploaded/JHFICMRVUX/ffts/XC454911-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/JHFICMRVUX/ffts/XC454911-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/JHFICMRVUX/ffts/XC454911-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/JHFICMRVUX/ffts/XC454911-full.png'}
2nd row{'small': '//www.xeno-canto.org/sounds/uploaded/PVQOLRXXWL/ffts/XC418340-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/PVQOLRXXWL/ffts/XC418340-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/PVQOLRXXWL/ffts/XC418340-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/PVQOLRXXWL/ffts/XC418340-full.png'}
3rd row{'small': '//www.xeno-canto.org/sounds/uploaded/BCFUZDOSJZ/ffts/XC291051-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/BCFUZDOSJZ/ffts/XC291051-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/BCFUZDOSJZ/ffts/XC291051-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/BCFUZDOSJZ/ffts/XC291051-full.png'}
4th row{'small': '//www.xeno-canto.org/sounds/uploaded/CDHIAMGTRT/ffts/XC283618-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/CDHIAMGTRT/ffts/XC283618-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/CDHIAMGTRT/ffts/XC283618-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/CDHIAMGTRT/ffts/XC283618-full.png'}
5th row{'small': '//www.xeno-canto.org/sounds/uploaded/LELYWQKUZX/ffts/XC209702-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/LELYWQKUZX/ffts/XC209702-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/LELYWQKUZX/ffts/XC209702-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/LELYWQKUZX/ffts/XC209702-full.png'}

Common Values

ValueCountFrequency (%)
{'small': '//www.xeno-canto.org/sounds/uploaded/YTUXOCTUEM/ffts/XC409005-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/YTUXOCTUEM/ffts/XC409005-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/YTUXOCTUEM/ffts/XC409005-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/YTUXOCTUEM/ffts/XC409005-full.png'}1
 
< 0.1%
{'small': '//www.xeno-canto.org/sounds/uploaded/OOECIWCSWV/ffts/XC219410-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/OOECIWCSWV/ffts/XC219410-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/OOECIWCSWV/ffts/XC219410-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/OOECIWCSWV/ffts/XC219410-full.png'}1
 
< 0.1%
{'small': '//www.xeno-canto.org/sounds/uploaded/PVQOLRXXWL/ffts/XC331991-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/PVQOLRXXWL/ffts/XC331991-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/PVQOLRXXWL/ffts/XC331991-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/PVQOLRXXWL/ffts/XC331991-full.png'}1
 
< 0.1%
{'small': '//www.xeno-canto.org/sounds/uploaded/OOECIWCSWV/ffts/XC220237-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/OOECIWCSWV/ffts/XC220237-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/OOECIWCSWV/ffts/XC220237-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/OOECIWCSWV/ffts/XC220237-full.png'}1
 
< 0.1%
{'small': '//www.xeno-canto.org/sounds/uploaded/ZQJCLMBULK/ffts/XC555363-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/ZQJCLMBULK/ffts/XC555363-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/ZQJCLMBULK/ffts/XC555363-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/ZQJCLMBULK/ffts/XC555363-full.png'}1
 
< 0.1%
{'small': '//www.xeno-canto.org/sounds/uploaded/AZXTZTQJRO/ffts/XC31231-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/AZXTZTQJRO/ffts/XC31231-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/AZXTZTQJRO/ffts/XC31231-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/AZXTZTQJRO/ffts/XC31231-full.png'}1
 
< 0.1%
{'small': '//www.xeno-canto.org/sounds/uploaded/HUTYLVCZFM/ffts/XC62926-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/HUTYLVCZFM/ffts/XC62926-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/HUTYLVCZFM/ffts/XC62926-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/HUTYLVCZFM/ffts/XC62926-full.png'}1
 
< 0.1%
{'small': '//www.xeno-canto.org/sounds/uploaded/PJVICFDZGZ/ffts/XC388612-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/PJVICFDZGZ/ffts/XC388612-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/PJVICFDZGZ/ffts/XC388612-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/PJVICFDZGZ/ffts/XC388612-full.png'}1
 
< 0.1%
{'small': '//www.xeno-canto.org/sounds/uploaded/EFJRFHSDLT/ffts/XC239858-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/EFJRFHSDLT/ffts/XC239858-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/EFJRFHSDLT/ffts/XC239858-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/EFJRFHSDLT/ffts/XC239858-full.png'}1
 
< 0.1%
{'small': '//www.xeno-canto.org/sounds/uploaded/WOEAFQRMUD/ffts/XC135070-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/WOEAFQRMUD/ffts/XC135070-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/WOEAFQRMUD/ffts/XC135070-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/WOEAFQRMUD/ffts/XC135070-full.png'}1
 
< 0.1%
Other values (5790)5790
99.8%

Length

2021-05-17T23:30:07.505494image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
small5800
 
12.5%
med5800
 
12.5%
large5800
 
12.5%
full5800
 
12.5%
www.xeno-canto.org/sounds/uploaded/ihioggzvqa/ffts/xc22037-small.png1
 
< 0.1%
www.xeno-canto.org/sounds/uploaded/rftxrybvbx/ffts/xc451010-med.png1
 
< 0.1%
www.xeno-canto.org/sounds/uploaded/jpbsnbuuef/ffts/xc163938-large.png1
 
< 0.1%
www.xeno-canto.org/sounds/uploaded/bwiydngphx/ffts/xc107466-med.png1
 
< 0.1%
www.xeno-canto.org/sounds/uploaded/kgocookttu/ffts/xc393305-small.png1
 
< 0.1%
www.xeno-canto.org/sounds/uploaded/eszpfeoqum/ffts/xc323307-large.png1
 
< 0.1%
Other values (23194)23194
50.0%

Most occurring characters

ValueCountFrequency (%)
/162400
 
8.5%
o116000
 
6.1%
'92800
 
4.9%
n92800
 
4.9%
s81200
 
4.3%
l81200
 
4.3%
d81200
 
4.3%
a69600
 
3.6%
w69600
 
3.6%
.69600
 
3.6%
Other values (52)993304
52.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1032400
54.1%
Other Punctuation365400
 
19.1%
Uppercase Letter278400
 
14.6%
Decimal Number134904
 
7.1%
Dash Punctuation46400
 
2.4%
Space Separator40600
 
2.1%
Open Punctuation5800
 
0.3%
Close Punctuation5800
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
X33404
 
12.0%
C32044
 
11.5%
V12640
 
4.5%
Z11856
 
4.3%
O11752
 
4.2%
P11632
 
4.2%
R10840
 
3.9%
B10392
 
3.7%
K10236
 
3.7%
D9880
 
3.5%
Other values (16)123724
44.4%
Lowercase Letter
ValueCountFrequency (%)
o116000
11.2%
n92800
 
9.0%
s81200
 
7.9%
l81200
 
7.9%
d81200
 
7.9%
a69600
 
6.7%
w69600
 
6.7%
e69600
 
6.7%
g58000
 
5.6%
u58000
 
5.6%
Other values (7)255200
24.7%
Decimal Number
ValueCountFrequency (%)
118268
13.5%
316408
12.2%
215020
11.1%
514168
10.5%
414140
10.5%
011880
8.8%
711744
8.7%
611428
8.5%
810976
8.1%
910872
8.1%
Other Punctuation
ValueCountFrequency (%)
/162400
44.4%
'92800
25.4%
.69600
19.0%
:23200
 
6.3%
,17400
 
4.8%
Open Punctuation
ValueCountFrequency (%)
{5800
100.0%
Space Separator
ValueCountFrequency (%)
40600
100.0%
Dash Punctuation
ValueCountFrequency (%)
-46400
100.0%
Close Punctuation
ValueCountFrequency (%)
}5800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1310800
68.6%
Common598904
31.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o116000
 
8.8%
n92800
 
7.1%
s81200
 
6.2%
l81200
 
6.2%
d81200
 
6.2%
a69600
 
5.3%
w69600
 
5.3%
e69600
 
5.3%
g58000
 
4.4%
u58000
 
4.4%
Other values (33)533600
40.7%
Common
ValueCountFrequency (%)
/162400
27.1%
'92800
15.5%
.69600
11.6%
-46400
 
7.7%
40600
 
6.8%
:23200
 
3.9%
118268
 
3.1%
,17400
 
2.9%
316408
 
2.7%
215020
 
2.5%
Other values (9)96808
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1909704
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/162400
 
8.5%
o116000
 
6.1%
'92800
 
4.9%
n92800
 
4.9%
s81200
 
4.3%
l81200
 
4.3%
d81200
 
4.3%
a69600
 
3.6%
w69600
 
3.6%
.69600
 
3.6%
Other values (52)993304
52.0%

lic
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size45.4 KiB
//creativecommons.org/licenses/by-nc-sa/4.0/
3569 
//creativecommons.org/licenses/by-nc-nd/2.5/
994 
//creativecommons.org/licenses/by-nc-sa/3.0/
862 
//creativecommons.org/licenses/by-nc-nd/4.0/
 
277
//creativecommons.org/licenses/by-sa/3.0/
 
68
Other values (4)
 
30

Length

Max length44
Median length44
Mean length43.95086207
Min length38

Characters and Unicode

Total characters254915
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row//creativecommons.org/licenses/by-nc-sa/4.0/
2nd row//creativecommons.org/licenses/by-nc-sa/4.0/
3rd row//creativecommons.org/licenses/by-nc-sa/4.0/
4th row//creativecommons.org/licenses/by-nc-sa/4.0/
5th row//creativecommons.org/licenses/by-nc-sa/4.0/

Common Values

ValueCountFrequency (%)
//creativecommons.org/licenses/by-nc-sa/4.0/3569
61.5%
//creativecommons.org/licenses/by-nc-nd/2.5/994
 
17.1%
//creativecommons.org/licenses/by-nc-sa/3.0/862
 
14.9%
//creativecommons.org/licenses/by-nc-nd/4.0/277
 
4.8%
//creativecommons.org/licenses/by-sa/3.0/68
 
1.2%
//creativecommons.org/licenses/by-sa/4.0/24
 
0.4%
//creativecommons.org/licenses/by-nc-nd/3.0/4
 
0.1%
//creativecommons.org/licenses/by-nc/4.0/1
 
< 0.1%
//creativecommons.org/licenses/by/4.0/1
 
< 0.1%

Length

2021-05-17T23:30:07.890544image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-17T23:30:08.020845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
creativecommons.org/licenses/by-nc-sa/4.03569
61.5%
creativecommons.org/licenses/by-nc-nd/2.5994
 
17.1%
creativecommons.org/licenses/by-nc-sa/3.0862
 
14.9%
creativecommons.org/licenses/by-nc-nd/4.0277
 
4.8%
creativecommons.org/licenses/by-sa/3.068
 
1.2%
creativecommons.org/licenses/by-sa/4.024
 
0.4%
creativecommons.org/licenses/by-nc-nd/3.04
 
0.1%
creativecommons.org/licenses/by/4.01
 
< 0.1%
creativecommons.org/licenses/by-nc/4.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
/34800
13.7%
e23200
 
9.1%
c23107
 
9.1%
s21923
 
8.6%
n18582
 
7.3%
o17400
 
6.8%
r11600
 
4.6%
i11600
 
4.6%
m11600
 
4.6%
.11600
 
4.6%
Other values (14)69503
27.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter185410
72.7%
Other Punctuation46400
 
18.2%
Decimal Number11600
 
4.6%
Dash Punctuation11505
 
4.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e23200
12.5%
c23107
12.5%
s21923
11.8%
n18582
10.0%
o17400
9.4%
r11600
 
6.3%
i11600
 
6.3%
m11600
 
6.3%
a10323
 
5.6%
t5800
 
3.1%
Other values (6)30275
16.3%
Decimal Number
ValueCountFrequency (%)
04806
41.4%
43872
33.4%
2994
 
8.6%
5994
 
8.6%
3934
 
8.1%
Other Punctuation
ValueCountFrequency (%)
/34800
75.0%
.11600
 
25.0%
Dash Punctuation
ValueCountFrequency (%)
-11505
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin185410
72.7%
Common69505
 
27.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e23200
12.5%
c23107
12.5%
s21923
11.8%
n18582
10.0%
o17400
9.4%
r11600
 
6.3%
i11600
 
6.3%
m11600
 
6.3%
a10323
 
5.6%
t5800
 
3.1%
Other values (6)30275
16.3%
Common
ValueCountFrequency (%)
/34800
50.1%
.11600
 
16.7%
-11505
 
16.6%
04806
 
6.9%
43872
 
5.6%
2994
 
1.4%
5994
 
1.4%
3934
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII254915
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/34800
13.7%
e23200
 
9.1%
c23107
 
9.1%
s21923
 
8.6%
n18582
 
7.3%
o17400
 
6.8%
r11600
 
4.6%
i11600
 
4.6%
m11600
 
4.6%
.11600
 
4.6%
Other values (14)69503
27.3%

q
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.4 KiB
B
3698 
A
2102 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5800
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
B3698
63.8%
A2102
36.2%

Length

2021-05-17T23:30:08.398685image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-17T23:30:08.536146image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
b3698
63.8%
a2102
36.2%

Most occurring characters

ValueCountFrequency (%)
B3698
63.8%
A2102
36.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter5800
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B3698
63.8%
A2102
36.2%

Most occurring scripts

ValueCountFrequency (%)
Latin5800
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B3698
63.8%
A2102
36.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII5800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B3698
63.8%
A2102
36.2%

length
Categorical

HIGH CORRELATION

Distinct20
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size45.4 KiB
0:12
 
384
0:16
 
377
0:18
 
376
0:17
 
371
0:14
 
354
Other values (15)
3938 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters23200
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0:17
2nd row0:14
3rd row0:15
4th row0:10
5th row0:11

Common Values

ValueCountFrequency (%)
0:12384
 
6.6%
0:16377
 
6.5%
0:18376
 
6.5%
0:17371
 
6.4%
0:14354
 
6.1%
0:10344
 
5.9%
0:13343
 
5.9%
0:19337
 
5.8%
0:15328
 
5.7%
0:11327
 
5.6%
Other values (10)2259
38.9%

Length

2021-05-17T23:30:08.883890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0:12384
 
6.6%
0:16377
 
6.5%
0:18376
 
6.5%
0:17371
 
6.4%
0:14354
 
6.1%
0:10344
 
5.9%
0:13343
 
5.9%
0:19337
 
5.8%
0:15328
 
5.7%
0:11327
 
5.6%
Other values (10)2259
38.9%

Most occurring characters

ValueCountFrequency (%)
08414
36.3%
:5800
25.0%
13931
16.9%
7691
 
3.0%
6665
 
2.9%
8652
 
2.8%
4631
 
2.7%
9630
 
2.7%
5617
 
2.7%
3613
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17400
75.0%
Other Punctuation5800
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08414
48.4%
13931
22.6%
7691
 
4.0%
6665
 
3.8%
8652
 
3.7%
4631
 
3.6%
9630
 
3.6%
5617
 
3.5%
3613
 
3.5%
2556
 
3.2%
Other Punctuation
ValueCountFrequency (%)
:5800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common23200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08414
36.3%
:5800
25.0%
13931
16.9%
7691
 
3.0%
6665
 
2.9%
8652
 
2.8%
4631
 
2.7%
9630
 
2.7%
5617
 
2.7%
3613
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII23200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08414
36.3%
:5800
25.0%
13931
16.9%
7691
 
3.0%
6665
 
2.9%
8652
 
2.8%
4631
 
2.7%
9630
 
2.7%
5617
 
2.7%
3613
 
2.6%

time
Date

MISSING

Distinct756
Distinct (%)15.0%
Missing765
Missing (%)13.2%
Memory size45.4 KiB
Minimum1900-01-01 00:00:00
Maximum1900-01-01 23:40:00
2021-05-17T23:30:09.172635image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:30:09.410464image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

date
Date

Distinct2361
Distinct (%)40.9%
Missing30
Missing (%)0.5%
Memory size45.4 KiB
Minimum1985-06-20 00:00:00
Maximum2021-05-05 00:00:00
2021-05-17T23:30:09.600293image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:30:09.796333image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

uploaded
Categorical

HIGH CARDINALITY

Distinct1868
Distinct (%)32.2%
Missing0
Missing (%)0.0%
Memory size45.4 KiB
2008-11-20
 
261
2015-03-23
 
65
2015-03-22
 
49
2013-05-30
 
33
2015-12-29
 
32
Other values (1863)
5360 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters58000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique783 ?
Unique (%)13.5%

Sample

1st row2019-02-04
2nd row2018-06-03
3rd row2015-11-18
4th row2015-10-03
5th row2015-01-09

Common Values

ValueCountFrequency (%)
2008-11-20261
 
4.5%
2015-03-2365
 
1.1%
2015-03-2249
 
0.8%
2013-05-3033
 
0.6%
2015-12-2932
 
0.6%
2011-07-1829
 
0.5%
2016-06-2027
 
0.5%
2011-01-1327
 
0.5%
2013-04-1924
 
0.4%
2014-02-2422
 
0.4%
Other values (1858)5231
90.2%

Length

2021-05-17T23:30:10.672040image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2008-11-20261
 
4.5%
2015-03-2365
 
1.1%
2015-03-2249
 
0.8%
2013-05-3033
 
0.6%
2015-12-2932
 
0.6%
2011-07-1829
 
0.5%
2016-06-2027
 
0.5%
2011-01-1327
 
0.5%
2013-04-1924
 
0.4%
2014-02-2422
 
0.4%
Other values (1858)5231
90.2%

Most occurring characters

ValueCountFrequency (%)
014478
25.0%
-11600
20.0%
210203
17.6%
19417
16.2%
52276
 
3.9%
32094
 
3.6%
61759
 
3.0%
91601
 
2.8%
41581
 
2.7%
71542
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number46400
80.0%
Dash Punctuation11600
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014478
31.2%
210203
22.0%
19417
20.3%
52276
 
4.9%
32094
 
4.5%
61759
 
3.8%
91601
 
3.5%
41581
 
3.4%
71542
 
3.3%
81449
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
-11600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common58000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014478
25.0%
-11600
20.0%
210203
17.6%
19417
16.2%
52276
 
3.9%
32094
 
3.6%
61759
 
3.0%
91601
 
2.8%
41581
 
2.7%
71542
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII58000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014478
25.0%
-11600
20.0%
210203
17.6%
19417
16.2%
52276
 
3.9%
32094
 
3.6%
61759
 
3.0%
91601
 
2.8%
41581
 
2.7%
71542
 
2.7%

also
Categorical

HIGH CARDINALITY

Distinct1089
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size45.4 KiB
['']
3887 
['Agelaius phoeniceus']
 
53
['Cardinalis cardinalis']
 
40
['Turdus migratorius']
 
29
['Melospiza melodia']
 
22
Other values (1084)
1769 

Length

Max length204
Median length4
Mean length15.61224138
Min length4

Characters and Unicode

Total characters90551
Distinct characters53
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique891 ?
Unique (%)15.4%

Sample

1st row['Cygnus buccinator']
2nd row['Agelaius phoeniceus']
3rd row['']
4th row['']
5th row['Poecile atricapillus']

Common Values

ValueCountFrequency (%)
['']3887
67.0%
['Agelaius phoeniceus']53
 
0.9%
['Cardinalis cardinalis']40
 
0.7%
['Turdus migratorius']29
 
0.5%
['Melospiza melodia']22
 
0.4%
['Cyanocitta cristata']21
 
0.4%
['Poecile atricapillus']19
 
0.3%
['Geothlypis trichas']18
 
0.3%
['Haemorhous mexicanus']18
 
0.3%
['Thryothorus ludovicianus']18
 
0.3%
Other values (1079)1675
28.9%

Length

2021-05-17T23:30:11.261941image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3887
36.6%
cardinalis305
 
2.9%
setophaga208
 
2.0%
agelaius145
 
1.4%
phoeniceus145
 
1.4%
melospiza128
 
1.2%
vireo126
 
1.2%
turdus117
 
1.1%
migratorius117
 
1.1%
melodia106
 
1.0%
Other values (453)5329
50.2%

Most occurring characters

ValueCountFrequency (%)
'14500
16.0%
a6835
 
7.5%
i5898
 
6.5%
[5800
 
6.4%
]5800
 
6.4%
s4926
 
5.4%
4813
 
5.3%
e4134
 
4.6%
o4027
 
4.4%
r3853
 
4.3%
Other values (43)29965
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter54825
60.5%
Other Punctuation15950
 
17.6%
Open Punctuation5800
 
6.4%
Close Punctuation5800
 
6.4%
Space Separator4813
 
5.3%
Uppercase Letter3363
 
3.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a6835
12.5%
i5898
10.8%
s4926
 
9.0%
e4134
 
7.5%
o4027
 
7.3%
r3853
 
7.0%
l3650
 
6.7%
u3516
 
6.4%
n2830
 
5.2%
c2777
 
5.1%
Other values (16)12379
22.6%
Uppercase Letter
ValueCountFrequency (%)
C609
18.1%
S536
15.9%
P422
12.5%
T326
9.7%
M313
9.3%
A212
 
6.3%
Z152
 
4.5%
V137
 
4.1%
H111
 
3.3%
B105
 
3.1%
Other values (12)440
13.1%
Other Punctuation
ValueCountFrequency (%)
'14500
90.9%
,1450
 
9.1%
Open Punctuation
ValueCountFrequency (%)
[5800
100.0%
Space Separator
ValueCountFrequency (%)
4813
100.0%
Close Punctuation
ValueCountFrequency (%)
]5800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin58188
64.3%
Common32363
35.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a6835
11.7%
i5898
 
10.1%
s4926
 
8.5%
e4134
 
7.1%
o4027
 
6.9%
r3853
 
6.6%
l3650
 
6.3%
u3516
 
6.0%
n2830
 
4.9%
c2777
 
4.8%
Other values (38)15742
27.1%
Common
ValueCountFrequency (%)
'14500
44.8%
[5800
 
17.9%
]5800
 
17.9%
4813
 
14.9%
,1450
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII90551
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
'14500
16.0%
a6835
 
7.5%
i5898
 
6.5%
[5800
 
6.4%
]5800
 
6.4%
s4926
 
5.4%
4813
 
5.3%
e4134
 
4.6%
o4027
 
4.4%
r3853
 
4.3%
Other values (43)29965
33.1%

rmk
Categorical

HIGH CARDINALITY
MISSING

Distinct3336
Distinct (%)88.0%
Missing2007
Missing (%)34.6%
Memory size45.4 KiB
Editing: High-pass filter, cutoff frequency 1kHz, some amplification. Equipment: Olympus WS-822 digital recorder with Audio-technica ATR 6550 shotgun.
 
80
Recording amplified.
 
28
Natural vocalization
 
25
song
 
22
natural vocalization
 
21
Other values (3331)
3617 

Length

Max length1358
Median length68
Mean length109.1386765
Min length4

Characters and Unicode

Total characters413963
Distinct characters99
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3176 ?
Unique (%)83.7%

Sample

1st rowMixed flock of Trumpeter Swans and Canada Geese feeding in an agricultural field. Recording of Swan's here XC454910
2nd rowNatural vocalization
3rd rowFlock calling while flying over at dusk. Amplification, low and high pass filters used in Audacity.
4th rowNatural vocalizations from a pair of birds in flight. Recording not modified.
5th rowA large flock of Canada Geese taking off. I believe most of the birds in this flock were parvipes ("Lesser") Canada Geese, but there were also larger (subspecies moffitti?) Canada Geese and a few Cackling Geese (several of the subspecies hutchinsii and possibly one of the subspecies minima) in the general vicinity. In the old days this would have been an "obvious" or "easy" flock of "Canada Geese." Now we're dealing with perhaps two species and probably two or three subspecies in the recording. Again, I believe most of the birds audible here are parvipes ("Lesser") Canada Geese.

Common Values

ValueCountFrequency (%)
Editing: High-pass filter, cutoff frequency 1kHz, some amplification. Equipment: Olympus WS-822 digital recorder with Audio-technica ATR 6550 shotgun.80
 
1.4%
Recording amplified.28
 
0.5%
Natural vocalization25
 
0.4%
song22
 
0.4%
natural vocalization21
 
0.4%
Amplification, low and high pass filters used in Audacity.20
 
0.3%
Recording amplified. High pass filter 6db.13
 
0.2%
(Sennheiser ME62 omni mic, 24 inch Roché parabola, Marantz PMD 661).11
 
0.2%
not seen calling among about twenty coots feeding in marsh9
 
0.2%
Recording amplified. High pass filter.8
 
0.1%
Other values (3326)3556
61.3%
(Missing)2007
34.6%

Length

2021-05-17T23:30:11.807130image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a2185
 
3.3%
the2184
 
3.3%
in1989
 
3.0%
of1568
 
2.4%
and1477
 
2.2%
from1096
 
1.7%
bird938
 
1.4%
to811
 
1.2%
with781
 
1.2%
was662
 
1.0%
Other values (5948)52490
79.3%

Most occurring characters

ValueCountFrequency (%)
62483
15.1%
e33135
 
8.0%
a26322
 
6.4%
i26147
 
6.3%
o22685
 
5.5%
t22487
 
5.4%
n22222
 
5.4%
r21670
 
5.2%
s17601
 
4.3%
d15339
 
3.7%
Other values (89)143872
34.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter306729
74.1%
Space Separator62483
 
15.1%
Uppercase Letter15672
 
3.8%
Other Punctuation12713
 
3.1%
Decimal Number10336
 
2.5%
Control2774
 
0.7%
Dash Punctuation1597
 
0.4%
Open Punctuation683
 
0.2%
Close Punctuation682
 
0.2%
Math Symbol258
 
0.1%
Other values (3)36
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e33135
 
10.8%
a26322
 
8.6%
i26147
 
8.5%
o22685
 
7.4%
t22487
 
7.3%
n22222
 
7.2%
r21670
 
7.1%
s17601
 
5.7%
d15339
 
5.0%
l14858
 
4.8%
Other values (19)84263
27.5%
Uppercase Letter
ValueCountFrequency (%)
A1779
 
11.4%
S1534
 
9.8%
C1194
 
7.6%
R1161
 
7.4%
T1138
 
7.3%
I868
 
5.5%
H837
 
5.3%
B830
 
5.3%
M786
 
5.0%
P731
 
4.7%
Other values (16)4814
30.7%
Other Punctuation
ValueCountFrequency (%)
.6155
48.4%
,2729
21.5%
/1128
 
8.9%
:997
 
7.8%
'448
 
3.5%
"443
 
3.5%
;305
 
2.4%
#171
 
1.3%
%146
 
1.1%
?88
 
0.7%
Other values (4)103
 
0.8%
Decimal Number
ValueCountFrequency (%)
01970
19.1%
21598
15.5%
11464
14.2%
51169
11.3%
3832
8.0%
6813
7.9%
4791
7.7%
7632
 
6.1%
8575
 
5.6%
9492
 
4.8%
Math Symbol
ValueCountFrequency (%)
=92
35.7%
~70
27.1%
<32
 
12.4%
>31
 
12.0%
|21
 
8.1%
+12
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
-1578
98.8%
18
 
1.1%
1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
(660
96.6%
[23
 
3.4%
Close Punctuation
ValueCountFrequency (%)
)659
96.6%
]23
 
3.4%
Control
ValueCountFrequency (%)
1387
50.0%
1387
50.0%
Final Punctuation
ValueCountFrequency (%)
17
70.8%
7
29.2%
Space Separator
ValueCountFrequency (%)
62483
100.0%
Connector Punctuation
ValueCountFrequency (%)
_11
100.0%
Other Symbol
ValueCountFrequency (%)
°1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin322401
77.9%
Common91562
 
22.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e33135
 
10.3%
a26322
 
8.2%
i26147
 
8.1%
o22685
 
7.0%
t22487
 
7.0%
n22222
 
6.9%
r21670
 
6.7%
s17601
 
5.5%
d15339
 
4.8%
l14858
 
4.6%
Other values (45)99935
31.0%
Common
ValueCountFrequency (%)
62483
68.2%
.6155
 
6.7%
,2729
 
3.0%
01970
 
2.2%
21598
 
1.7%
-1578
 
1.7%
11464
 
1.6%
1387
 
1.5%
1387
 
1.5%
51169
 
1.3%
Other values (34)9642
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII413848
> 99.9%
Latin 1 Sup72
 
< 0.1%
Punctuation43
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
62483
15.1%
e33135
 
8.0%
a26322
 
6.4%
i26147
 
6.3%
o22685
 
5.5%
t22487
 
5.4%
n22222
 
5.4%
r21670
 
5.2%
s17601
 
4.3%
d15339
 
3.7%
Other values (81)143757
34.7%
Punctuation
ValueCountFrequency (%)
18
41.9%
17
39.5%
7
 
16.3%
1
 
2.3%
Latin 1 Sup
ValueCountFrequency (%)
é47
65.3%
ö16
 
22.2%
ø8
 
11.1%
°1
 
1.4%

bird-seen
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.4 KiB
yes
3505 
unknown
1263 
no
1032 

Length

Max length7
Median length3
Mean length3.693103448
Min length2

Characters and Unicode

Total characters21420
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowyes
3rd rowyes
4th rowyes
5th rowyes

Common Values

ValueCountFrequency (%)
yes3505
60.4%
unknown1263
 
21.8%
no1032
 
17.8%

Length

2021-05-17T23:30:12.141516image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-17T23:30:12.232196image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
yes3505
60.4%
unknown1263
 
21.8%
no1032
 
17.8%

Most occurring characters

ValueCountFrequency (%)
n4821
22.5%
y3505
16.4%
e3505
16.4%
s3505
16.4%
o2295
10.7%
u1263
 
5.9%
k1263
 
5.9%
w1263
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter21420
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n4821
22.5%
y3505
16.4%
e3505
16.4%
s3505
16.4%
o2295
10.7%
u1263
 
5.9%
k1263
 
5.9%
w1263
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Latin21420
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n4821
22.5%
y3505
16.4%
e3505
16.4%
s3505
16.4%
o2295
10.7%
u1263
 
5.9%
k1263
 
5.9%
w1263
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII21420
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n4821
22.5%
y3505
16.4%
e3505
16.4%
s3505
16.4%
o2295
10.7%
u1263
 
5.9%
k1263
 
5.9%
w1263
 
5.9%

playback-used
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.4 KiB
no
4420 
unknown
1171 
yes
 
209

Length

Max length7
Median length2
Mean length3.045517241
Min length2

Characters and Unicode

Total characters17664
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no4420
76.2%
unknown1171
 
20.2%
yes209
 
3.6%

Length

2021-05-17T23:30:12.471856image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-17T23:30:12.557416image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
no4420
76.2%
unknown1171
 
20.2%
yes209
 
3.6%

Most occurring characters

ValueCountFrequency (%)
n7933
44.9%
o5591
31.7%
u1171
 
6.6%
k1171
 
6.6%
w1171
 
6.6%
y209
 
1.2%
e209
 
1.2%
s209
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter17664
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n7933
44.9%
o5591
31.7%
u1171
 
6.6%
k1171
 
6.6%
w1171
 
6.6%
y209
 
1.2%
e209
 
1.2%
s209
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Latin17664
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n7933
44.9%
o5591
31.7%
u1171
 
6.6%
k1171
 
6.6%
w1171
 
6.6%
y209
 
1.2%
e209
 
1.2%
s209
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII17664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n7933
44.9%
o5591
31.7%
u1171
 
6.6%
k1171
 
6.6%
w1171
 
6.6%
y209
 
1.2%
e209
 
1.2%
s209
 
1.2%

pred
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.4 KiB
1
3181 
0
2619 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5800
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
13181
54.8%
02619
45.2%

Length

2021-05-17T23:30:12.792056image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-17T23:30:12.882357image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
13181
54.8%
02619
45.2%

Most occurring characters

ValueCountFrequency (%)
13181
54.8%
02619
45.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5800
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
13181
54.8%
02619
45.2%

Most occurring scripts

ValueCountFrequency (%)
Common5800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
13181
54.8%
02619
45.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII5800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13181
54.8%
02619
45.2%

gender
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.5%
Missing5369
Missing (%)92.6%
Memory size45.4 KiB
male
366 
female
65 

Length

Max length6
Median length4
Mean length4.30162413
Min length4

Characters and Unicode

Total characters1854
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowmale
3rd rowfemale
4th rowfemale
5th rowfemale

Common Values

ValueCountFrequency (%)
male366
 
6.3%
female65
 
1.1%
(Missing)5369
92.6%

Length

2021-05-17T23:30:13.145315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-17T23:30:13.257536image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
male366
84.9%
female65
 
15.1%

Most occurring characters

ValueCountFrequency (%)
e496
26.8%
m431
23.2%
a431
23.2%
l431
23.2%
f65
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1854
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e496
26.8%
m431
23.2%
a431
23.2%
l431
23.2%
f65
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Latin1854
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e496
26.8%
m431
23.2%
a431
23.2%
l431
23.2%
f65
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1854
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e496
26.8%
m431
23.2%
a431
23.2%
l431
23.2%
f65
 
3.5%

age
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.4%
Missing5350
Missing (%)92.2%
Memory size45.4 KiB
adult
402 
juvenile
48 

Length

Max length8
Median length5
Mean length5.32
Min length5

Characters and Unicode

Total characters2394
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowadult
2nd rowadult
3rd rowadult
4th rowadult
5th rowjuvenile

Common Values

ValueCountFrequency (%)
adult402
 
6.9%
juvenile48
 
0.8%
(Missing)5350
92.2%

Length

2021-05-17T23:30:13.539938image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-17T23:30:13.650546image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
adult402
89.3%
juvenile48
 
10.7%

Most occurring characters

ValueCountFrequency (%)
u450
18.8%
l450
18.8%
a402
16.8%
d402
16.8%
t402
16.8%
e96
 
4.0%
j48
 
2.0%
v48
 
2.0%
n48
 
2.0%
i48
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2394
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u450
18.8%
l450
18.8%
a402
16.8%
d402
16.8%
t402
16.8%
e96
 
4.0%
j48
 
2.0%
v48
 
2.0%
n48
 
2.0%
i48
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2394
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u450
18.8%
l450
18.8%
a402
16.8%
d402
16.8%
t402
16.8%
e96
 
4.0%
j48
 
2.0%
v48
 
2.0%
n48
 
2.0%
i48
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u450
18.8%
l450
18.8%
a402
16.8%
d402
16.8%
t402
16.8%
e96
 
4.0%
j48
 
2.0%
v48
 
2.0%
n48
 
2.0%
i48
 
2.0%

month
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)0.2%
Missing30
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean5.706412478
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.4 KiB
2021-05-17T23:30:13.745336image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q37
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.569966095
Coefficient of variation (CV)0.4503645863
Kurtosis0.15801253
Mean5.706412478
Median Absolute Deviation (MAD)1
Skewness0.639067317
Sum32926
Variance6.60472573
MonotonicityNot monotonic
2021-05-17T23:30:13.876534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
51305
22.5%
61054
18.2%
4879
15.2%
7440
 
7.6%
3408
 
7.0%
2313
 
5.4%
8307
 
5.3%
9225
 
3.9%
12211
 
3.6%
11210
 
3.6%
Other values (2)418
 
7.2%
ValueCountFrequency (%)
1208
 
3.6%
2313
 
5.4%
3408
 
7.0%
4879
15.2%
51305
22.5%
61054
18.2%
7440
 
7.6%
8307
 
5.3%
9225
 
3.9%
10210
 
3.6%
ValueCountFrequency (%)
12211
 
3.6%
11210
 
3.6%
10210
 
3.6%
9225
 
3.9%
8307
 
5.3%
7440
 
7.6%
61054
18.2%
51305
22.5%
4879
15.2%
3408
 
7.0%

day
Real number (ℝ≥0)

Distinct31
Distinct (%)0.5%
Missing30
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean15.6389948
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.4 KiB
2021-05-17T23:30:14.031395image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.826823573
Coefficient of variation (CV)0.564411184
Kurtosis-1.206424945
Mean15.6389948
Median Absolute Deviation (MAD)8
Skewness-0.02488560941
Sum90237
Variance77.91281438
MonotonicityNot monotonic
2021-05-17T23:30:14.206493image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1238
 
4.1%
3225
 
3.9%
23222
 
3.8%
20214
 
3.7%
27208
 
3.6%
25207
 
3.6%
29204
 
3.5%
4203
 
3.5%
17202
 
3.5%
21197
 
3.4%
Other values (21)3650
62.9%
ValueCountFrequency (%)
1238
4.1%
2163
2.8%
3225
3.9%
4203
3.5%
5175
3.0%
6182
3.1%
7188
3.2%
8163
2.8%
9191
3.3%
10185
3.2%
ValueCountFrequency (%)
3187
 
1.5%
30144
2.5%
29204
3.5%
28172
3.0%
27208
3.6%
26171
2.9%
25207
3.6%
24196
3.4%
23222
3.8%
22168
2.9%

hour
Real number (ℝ≥0)

MISSING

Distinct24
Distinct (%)0.5%
Missing765
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean10.1388282
Minimum0
Maximum23
Zeros12
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size45.4 KiB
2021-05-17T23:30:14.362087image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q18
median9
Q312
95-th percentile18
Maximum23
Range23
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.727986882
Coefficient of variation (CV)0.3676940577
Kurtosis0.5164947252
Mean10.1388282
Median Absolute Deviation (MAD)2
Skewness0.9546544093
Sum51049
Variance13.89788619
MonotonicityNot monotonic
2021-05-17T23:30:14.524401image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
8803
13.8%
7731
12.6%
9686
11.8%
10566
9.8%
11380
6.6%
6366
6.3%
12250
 
4.3%
13197
 
3.4%
14178
 
3.1%
17154
 
2.7%
Other values (14)724
12.5%
(Missing)765
13.2%
ValueCountFrequency (%)
012
 
0.2%
17
 
0.1%
25
 
0.1%
39
 
0.2%
414
 
0.2%
5106
 
1.8%
6366
6.3%
7731
12.6%
8803
13.8%
9686
11.8%
ValueCountFrequency (%)
235
 
0.1%
2212
 
0.2%
2124
 
0.4%
2052
 
0.9%
1996
1.7%
18129
2.2%
17154
2.7%
16133
2.3%
15120
2.1%
14178
3.1%

minute
Real number (ℝ≥0)

MISSING
ZEROS

Distinct60
Distinct (%)1.2%
Missing765
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean21.3469712
Minimum0
Maximum59
Zeros1468
Zeros (%)25.3%
Negative0
Negative (%)0.0%
Memory size45.4 KiB
2021-05-17T23:30:14.700405image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median26
Q330
95-th percentile52
Maximum59
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.86641399
Coefficient of variation (CV)0.8369531124
Kurtosis-1.141881215
Mean21.3469712
Median Absolute Deviation (MAD)16
Skewness0.1936456324
Sum107482
Variance319.2087488
MonotonicityNot monotonic
2021-05-17T23:30:14.886646image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01468
25.3%
301188
20.5%
15122
 
2.1%
45116
 
2.0%
20104
 
1.8%
4094
 
1.6%
1080
 
1.4%
5076
 
1.3%
563
 
1.1%
2560
 
1.0%
Other values (50)1664
28.7%
(Missing)765
13.2%
ValueCountFrequency (%)
01468
25.3%
125
 
0.4%
237
 
0.6%
327
 
0.5%
413
 
0.2%
563
 
1.1%
639
 
0.7%
736
 
0.6%
825
 
0.4%
933
 
0.6%
ValueCountFrequency (%)
5931
0.5%
5843
0.7%
5722
 
0.4%
5645
0.8%
5537
0.6%
5432
0.6%
5331
0.5%
5230
 
0.5%
5130
 
0.5%
5076
1.3%

Interactions

2021-05-17T23:29:36.375120image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:36.836348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:37.376101image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:37.743658image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:38.390599image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:38.756427image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:39.014468image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:39.244111image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:39.438330image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:39.628108image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:39.808055image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:39.993912image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:40.144687image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:40.379165image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:40.636213image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:40.848827image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:41.286619image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:41.919410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:42.473801image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:42.706137image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:42.956477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:43.143721image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:43.738958image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:44.671395image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:45.146934image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:45.596659image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:45.909283image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:46.243759image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:46.571314image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:46.868628image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:47.256851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:47.715803image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:48.182681image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:48.552903image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:48.953229image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:49.445414image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:49.832167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:50.431258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:50.899520image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:51.344407image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:51.679057image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:52.361069image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:52.718804image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:53.071522image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:53.329159image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:53.549867image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:53.783716image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:53.984453image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:54.256714image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:54.438561image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:54.597837image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:54.742083image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:54.915022image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:55.073352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:55.252541image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:55.397369image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:55.530603image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:55.842575image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:55.988945image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:56.190784image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:56.373338image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:56.538564image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:56.707479image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-17T23:29:56.878224image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-05-17T23:30:15.227304image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-17T23:30:15.485648image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-17T23:30:15.679167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-17T23:30:15.960151image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-17T23:30:16.203908image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-17T23:29:57.406554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-17T23:29:58.734679image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-05-17T23:29:59.231132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-05-17T23:29:59.579360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexidgenspsspenreccntloclatlngalttypeurlfilefile-namesonolicqlengthtimedateuploadedalsormkbird-seenplayback-usedpredgenderagemonthdayhourminute
096454911BrantacanadensisNaNCanada GooseBruce LagerquistUnited StatesSedro-Woolley, Skagit County, Washington48.5237-122.018530call//www.xeno-canto.org/454911//www.xeno-canto.org/454911/downloadXC454911-190202_02 Canadian Geese.mp3{'small': '//www.xeno-canto.org/sounds/uploaded/JHFICMRVUX/ffts/XC454911-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/JHFICMRVUX/ffts/XC454911-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/JHFICMRVUX/ffts/XC454911-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/JHFICMRVUX/ffts/XC454911-full.png'}//creativecommons.org/licenses/by-nc-sa/4.0/A0:171900-01-01 11:30:002019-02-022019-02-04['Cygnus buccinator']Mixed flock of Trumpeter Swans and Canada Geese feeding in an agricultural field. Recording of Swan's here XC454910yesno1NaNNaN2.02.011.030.0
197418340BrantacanadensisNaNCanada GooseSue RiffeUnited StatesAu Sable SF - Big Creek Rd, Michigan44.0185-83.7560180song//www.xeno-canto.org/418340//www.xeno-canto.org/418340/downloadXC418340-Canada Goose on 5.11.18 at Au Sable SF MI at 11.20 for .14 _0908 .mp3{'small': '//www.xeno-canto.org/sounds/uploaded/PVQOLRXXWL/ffts/XC418340-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/PVQOLRXXWL/ffts/XC418340-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/PVQOLRXXWL/ffts/XC418340-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/PVQOLRXXWL/ffts/XC418340-full.png'}//creativecommons.org/licenses/by-nc-sa/4.0/A0:141900-01-01 11:20:002018-05-112018-06-03['Agelaius phoeniceus']Natural vocalizationyesno0NaNNaN5.011.011.020.0
2107291051BrantacanadensisNaNCanada GooseEric HoughUnited StatesSan Juan River, Cottonwood Day-Use Area, Navajo Lake State Park, San Juan County, New Mexico36.8068-107.67891800call//www.xeno-canto.org/291051//www.xeno-canto.org/291051/downloadXC291051-CANG_11515_1730_SanJuanRiver-NavajoDam.mp3{'small': '//www.xeno-canto.org/sounds/uploaded/BCFUZDOSJZ/ffts/XC291051-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/BCFUZDOSJZ/ffts/XC291051-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/BCFUZDOSJZ/ffts/XC291051-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/BCFUZDOSJZ/ffts/XC291051-full.png'}//creativecommons.org/licenses/by-nc-sa/4.0/A0:151900-01-01 17:30:002015-11-152015-11-18['']Flock calling while flying over at dusk. Amplification, low and high pass filters used in Audacity.yesno1NaNNaN11.015.017.030.0
3108283618BrantacanadensisNaNCanada GooseGarrett MacDonaldUnited StatesBeluga--North Bog, Kenai Peninsula Borough, Alaska61.2089-151.010340call, flight call//www.xeno-canto.org/283618//www.xeno-canto.org/283618/downloadXC283618-LS100466.mp3{'small': '//www.xeno-canto.org/sounds/uploaded/CDHIAMGTRT/ffts/XC283618-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/CDHIAMGTRT/ffts/XC283618-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/CDHIAMGTRT/ffts/XC283618-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/CDHIAMGTRT/ffts/XC283618-full.png'}//creativecommons.org/licenses/by-nc-sa/4.0/A0:101900-01-01 11:00:002015-05-202015-10-03['']Natural vocalizations from a pair of birds in flight. Recording not modified.yesno1NaNNaN5.020.011.00.0
4110209702BrantacanadensisNaNCanada GooseAlbert @ Max lastukhinUnited StatesOyster Bay (near Lattingtown), Nassau, New York40.8881-73.585110call//www.xeno-canto.org/209702//www.xeno-canto.org/209702/downloadXC209702-Poecile atricapillus Dec_27,_2014,_4_05_PM,C1.mp3{'small': '//www.xeno-canto.org/sounds/uploaded/LELYWQKUZX/ffts/XC209702-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/LELYWQKUZX/ffts/XC209702-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/LELYWQKUZX/ffts/XC209702-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/LELYWQKUZX/ffts/XC209702-full.png'}//creativecommons.org/licenses/by-nc-sa/4.0/A0:111900-01-01 16:00:002014-12-272015-01-09['Poecile atricapillus']NaNyesno1NaNNaN12.027.016.00.0
5118165398BrantacanadensisparvipesCanada GooseTed FloydUnited StatesBoulder, Colorado40.0160-105.27651600call//www.xeno-canto.org/165398//www.xeno-canto.org/165398/downloadXC165398-CanG for Xeno-Canto.mp3{'small': '//www.xeno-canto.org/sounds/uploaded/KADPGEQPZI/ffts/XC165398-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/KADPGEQPZI/ffts/XC165398-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/KADPGEQPZI/ffts/XC165398-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/KADPGEQPZI/ffts/XC165398-full.png'}//creativecommons.org/licenses/by-nc-sa/3.0/A0:191900-01-01 09:30:002014-01-242014-01-25['']A large flock of Canada Geese taking off. I believe most of the birds in this flock were parvipes ("Lesser") Canada Geese, but there were also larger (subspecies moffitti?) Canada Geese and a few Cackling Geese (several of the subspecies hutchinsii and possibly one of the subspecies minima) in the general vicinity. \r\n\r\nIn the old days this would have been an "obvious" or "easy" flock of "Canada Geese." Now we're dealing with perhaps two species and probably two or three subspecies in the recording. Again, I believe most of the birds audible here are parvipes ("Lesser") Canada Geese.yesno1NaNNaN1.024.09.030.0
61291136BrantacanadensisNaNCanada GooseDon JonesUnited StatesBrace Road, Southampton, NJ39.9337-74.7170?song//www.xeno-canto.org/1136//www.xeno-canto.org/1136/downloadbird034.mp3{'small': '//www.xeno-canto.org/sounds/uploaded/BCWZQTGMSO/ffts/XC1136-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/BCWZQTGMSO/ffts/XC1136-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/BCWZQTGMSO/ffts/XC1136-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/BCWZQTGMSO/ffts/XC1136-full.png'}//creativecommons.org/licenses/by-nc-nd/2.5/A0:10NaT1997-10-172008-11-20['']NaNunknownunknown0NaNNaN10.017.0NaNNaN
7132536877BrantacanadensisNaNCanada GooseSue RiffeUnited StatesS Cape May Meadows, Cape May Cty, New Jersey38.9381-74.94460adult, call, sex uncertain//www.xeno-canto.org/536877//www.xeno-canto.org/536877/downloadXC536877-Canada Goose on 10.18.19 at S Cape May Meadows NJ at 18.52 for .19.mp3{'small': '//www.xeno-canto.org/sounds/uploaded/PVQOLRXXWL/ffts/XC536877-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/PVQOLRXXWL/ffts/XC536877-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/PVQOLRXXWL/ffts/XC536877-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/PVQOLRXXWL/ffts/XC536877-full.png'}//creativecommons.org/licenses/by-nc-sa/4.0/B0:191900-01-01 18:52:002019-10-182020-03-21['Charadrius vociferus']Natural vocalization of a flock of geese landing on the water near sunset. Windyyesno1NaNadult10.018.018.052.0
8133511453BrantacanadensisNaNCanada GoosePhoenix BirderUnited StatesGilbert, Maricopa County, Arizona33.3634-111.7341380adult, call, female, male//www.xeno-canto.org/511453//www.xeno-canto.org/511453/downloadXC511453-CaGo.2019.12.10.AZ.Maricopa.RiparianPreserve.mp3{'small': '//www.xeno-canto.org/sounds/uploaded/UKNISVRBBF/ffts/XC511453-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/UKNISVRBBF/ffts/XC511453-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/UKNISVRBBF/ffts/XC511453-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/UKNISVRBBF/ffts/XC511453-full.png'}//creativecommons.org/licenses/by-nc-sa/4.0/B0:191900-01-01 08:52:002019-12-102019-12-10['Toxostoma curvirostre']Sound Devices MixPre-3 Wildtronics Stereo Model #WTPMMSA 22” Parabolic Reflector, phoenixbirder@gmail.comyesno1maleadult12.010.08.052.0
9134504983BrantacanadensiscanadensisCanada Goosenick talbotUnited StatesCentral Park, New York city,USA40.7740-73.971020call//www.xeno-canto.org/504983//www.xeno-canto.org/504983/downloadXC504983-2019_10_21 Branta canadensis2.mp3{'small': '//www.xeno-canto.org/sounds/uploaded/CCUCXWCPSW/ffts/XC504983-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/CCUCXWCPSW/ffts/XC504983-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/CCUCXWCPSW/ffts/XC504983-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/CCUCXWCPSW/ffts/XC504983-full.png'}//creativecommons.org/licenses/by-nc-sa/4.0/B0:131900-01-01 13:00:002019-10-212019-10-30['']A pair of birds calling from a lakeyesno1NaNNaN10.021.013.00.0

Last rows

df_indexidgenspsspenreccntloclatlngalttypeurlfilefile-namesonolicqlengthtimedateuploadedalsormkbird-seenplayback-usedpredgenderagemonthdayhourminute
579056284469830SonusnaturalisNaNSoundscapeBates EstabrooksUnited StatesHeiskell, Anderson County, Tennessee36.1728-84.0080280call//www.xeno-canto.org/469830//www.xeno-canto.org/469830/downloadXC469830-MP-Unk DWC-043019.mp3{'small': '//www.xeno-canto.org/sounds/uploaded/NRJIPZPNRN/ffts/XC469830-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/NRJIPZPNRN/ffts/XC469830-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/NRJIPZPNRN/ffts/XC469830-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/NRJIPZPNRN/ffts/XC469830-full.png'}//creativecommons.org/licenses/by-nc-sa/4.0/B0:071900-01-01 15:00:002019-04-302019-05-01['']Need ID help with three-note "breet" at 0:2.1. Thanks.nono1NaNNaN4.030.015.00.0
579156285467481SonusnaturalisNaNSoundscapeBates EstabrooksUnited StatesAndersonville, Anderson County, Tennessee36.1934-83.9963280call//www.xeno-canto.org/467481//www.xeno-canto.org/467481/downloadXC467481-MP-Unk 1-Mt Olive-190418-006.mp3{'small': '//www.xeno-canto.org/sounds/uploaded/NRJIPZPNRN/ffts/XC467481-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/NRJIPZPNRN/ffts/XC467481-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/NRJIPZPNRN/ffts/XC467481-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/NRJIPZPNRN/ffts/XC467481-full.png'}//creativecommons.org/licenses/by-nc-sa/4.0/B0:181900-01-01 08:00:002019-04-182019-04-19['']Need ID on sharp two-note up-slide at 0:1.2 and 0:17.7. Thanks.nono1NaNNaN4.018.08.00.0
579256287463867SonusnaturalisNaNSoundscapeBrian HendrixUnited StatesHendrix Habitat - Fairview, Williamson County, Tennessee35.9870-87.1450260call//www.xeno-canto.org/463867//www.xeno-canto.org/463867/downloadXC463867-20190328-001.mp3{'small': '//www.xeno-canto.org/sounds/uploaded/WFZJIOWFQY/ffts/XC463867-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/WFZJIOWFQY/ffts/XC463867-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/WFZJIOWFQY/ffts/XC463867-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/WFZJIOWFQY/ffts/XC463867-full.png'}//creativecommons.org/licenses/by-nc-sa/4.0/B0:141900-01-01 15:30:002019-03-282019-03-28['']Recorded on: Zoom H4n Pro with internal mics\r\n\r\nCarolina Wren, Northern Cardinal, Tufted Titmouse, Pine Siskinnono1NaNNaN3.028.015.030.0
579356290439751SonusnaturalisNaNSoundscapeBates EstabrooksUnited StatesAndersonville, Anderson County, Tennessee36.1720-84.0070280call//www.xeno-canto.org/439751//www.xeno-canto.org/439751/downloadXC439751-Unk-DWC-20181022_162408.mp3{'small': '//www.xeno-canto.org/sounds/uploaded/NRJIPZPNRN/ffts/XC439751-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/NRJIPZPNRN/ffts/XC439751-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/NRJIPZPNRN/ffts/XC439751-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/NRJIPZPNRN/ffts/XC439751-full.png'}//creativecommons.org/licenses/by-nc-sa/4.0/B0:151900-01-01 16:00:002018-10-222018-10-23['']Eastern Gray Squirrelnono1NaNNaN10.022.016.00.0
579456292435682SonusnaturalisNaNSoundscapeThomas G. GravesUnited StatesMorro Bay, Morro Strand State Beach, San Luis Obispo County, California35.3975-120.86710call//www.xeno-canto.org/435682//www.xeno-canto.org/435682/downloadXC435682-20 Willet?, mix, Morro Bay, Alva Paul Beach, 9-8-18.mp3{'small': '//www.xeno-canto.org/sounds/uploaded/KGOCOOKTTU/ffts/XC435682-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/KGOCOOKTTU/ffts/XC435682-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/KGOCOOKTTU/ffts/XC435682-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/KGOCOOKTTU/ffts/XC435682-full.png'}//creativecommons.org/licenses/by-nc-sa/4.0/B0:101900-01-01 09:35:002018-09-082018-09-23['Tringa semipalmata', 'Limosa fedoa', 'Thalasseus elegans']Willet's :"laugh" call, Marbled Godwit's "gur-rik" call? Birds seen on sandy beach, taking off in flight as I walked by (Recorded with Marantz PMD 661, Sennheiser ME62, 24 inch Roché parabola).yesno1NaNNaN9.08.09.035.0
579556300410862SonusnaturalisNaNSoundscapeBates EstabrooksUnited StatesCaryville, Campbell County, Tennessee36.2974-84.2647900song//www.xeno-canto.org/410862//www.xeno-canto.org/410862/downloadXC410862-M-Unk-Cumberland Trailhead-180413-000.mp3{'small': '//www.xeno-canto.org/sounds/uploaded/NRJIPZPNRN/ffts/XC410862-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/NRJIPZPNRN/ffts/XC410862-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/NRJIPZPNRN/ffts/XC410862-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/NRJIPZPNRN/ffts/XC410862-full.png'}//creativecommons.org/licenses/by-nc-sa/4.0/B0:171900-01-01 08:00:002018-04-132018-04-15['Setophaga citrina', 'Pipilo erythrophthalmus', 'Sitta carolinensis', 'Mniotilta varia']I need ID help with the background song at ~1.5 and 11.5 secs. Consists of a several-note whistle starting at ~4Khz and sliding slightly downscale. Thanks.nono0NaNNaN4.013.08.00.0
579656328313197SonusnaturalisNaNSoundscapeBates EstabrooksUnited StatesTownsend, Blount County, Tennessee35.6293-83.7287500song//www.xeno-canto.org/313197//www.xeno-canto.org/313197/downloadXC313197-Schoolhouse Gap Unknown 2.mp3{'small': '//www.xeno-canto.org/sounds/uploaded/NRJIPZPNRN/ffts/XC313197-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/NRJIPZPNRN/ffts/XC313197-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/NRJIPZPNRN/ffts/XC313197-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/NRJIPZPNRN/ffts/XC313197-full.png'}//creativecommons.org/licenses/by-nc-sa/4.0/B0:121900-01-01 07:00:002016-04-212016-04-21['Setophaga virens', 'Parkesia motacilla', 'Baeolophus bicolor']NaNnono0NaNNaN4.021.07.00.0
579756340278258SonusnaturalisNaNSoundscapeBates EstabrooksUnited StatesCaryville, Campbell County, Tennessee36.2925-84.2624900call//www.xeno-canto.org/278258//www.xeno-canto.org/278258/downloadXC278258-GWM Unk-Audio recording 2015-09-07 08-28-38.mp3{'small': '//www.xeno-canto.org/sounds/uploaded/NRJIPZPNRN/ffts/XC278258-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/NRJIPZPNRN/ffts/XC278258-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/NRJIPZPNRN/ffts/XC278258-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/NRJIPZPNRN/ffts/XC278258-full.png'}//creativecommons.org/licenses/by-nc-sa/4.0/B0:171900-01-01 08:00:002015-09-072015-09-08['']Based on Forum comments, I now suspect this is an Eastern Chipmunk. (Edited 9-9-15.)nono1NaNNaN9.07.08.00.0
579856347277076SonusnaturalisNaNSoundscapeTerry DavisUnited States4 (near Mansfield), Red River Parish, Louisiana32.1232-93.471140song//www.xeno-canto.org/277076//www.xeno-canto.org/277076/downloadXC277076-05bevi06291516.mp3{'small': '//www.xeno-canto.org/sounds/uploaded/VREQOVRYQI/ffts/XC277076-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/VREQOVRYQI/ffts/XC277076-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/VREQOVRYQI/ffts/XC277076-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/VREQOVRYQI/ffts/XC277076-full.png'}//creativecommons.org/licenses/by-nc-sa/4.0/B0:111900-01-01 07:00:002015-06-292015-08-31['Cardinalis cardinalis', 'Spiza americana', 'Vireo bellii', 'Icterus galbula', 'Agelaius phoeniceus']Marker approximate. Poor short. Note Yellow-breasted chat/ Upland Sandpiper call after 0:1 Along with one or two of the earlier Upland Sandpiper-like calls, this particular one certainly sounds more like Yellow-breasted Chat to meunknownunknown0NaNNaN6.029.07.00.0
579956349276699SonusnaturalisNaNSoundscapeTerry DavisUnited States4 (near Mansfield), Red River Parish, Louisiana32.1251-93.471540song//www.xeno-canto.org/276699//www.xeno-canto.org/276699/downloadXC276699-07bevi06281548.mp3{'small': '//www.xeno-canto.org/sounds/uploaded/VREQOVRYQI/ffts/XC276699-small.png', 'med': '//www.xeno-canto.org/sounds/uploaded/VREQOVRYQI/ffts/XC276699-med.png', 'large': '//www.xeno-canto.org/sounds/uploaded/VREQOVRYQI/ffts/XC276699-large.png', 'full': '//www.xeno-canto.org/sounds/uploaded/VREQOVRYQI/ffts/XC276699-full.png'}//creativecommons.org/licenses/by-nc-sa/4.0/B0:101900-01-01 09:56:002015-06-282015-08-28['Spiza americana', 'Molothrus ater', 'Vireo bellii', 'Pipilo erythrophthalmus', 'Icteria virens', 'Icterus spurius']Fol xc268694. Poor shortunknownno0NaNNaN6.028.09.056.0